The List of 110 Selected Individuals

All the analysis will be performed on the 110 individuals which were selected based on multiple criteria such as 1) they are either normo-glycimic or hyper-glycemic / T2D individuals, 2) they have information from all the 4 OMICS (methylation, transcriptomics, phenotypes, genotypes), 3) they all fall within the same age category, and a few other minor criteria. Now we will read the list of those 110 individuals and display a few of them:

set.seed(1)
selected_ind<-scan("OVERLAPPING_110_SAMPLES_4OMICS.txt", what = "charater")
selected_ind<-paste0("ID",selected_ind)
print(head(selected_ind, 20))
##  [1] "ID1"  "ID3"  "ID4"  "ID6"  "ID8"  "ID10" "ID14" "ID19" "ID21" "ID30"
## [11] "ID32" "ID34" "ID35" "ID36" "ID38" "ID39" "ID44" "ID46" "ID55" "ID58"
print(tail(selected_ind, 20))
##  [1] "ID136" "ID156" "ID160" "ID165" "ID168" "ID172" "ID175" "ID182"
##  [9] "ID183" "ID191" "ID194" "ID200" "ID209" "ID210" "ID214" "ID217"
## [17] "ID221" "ID228" "ID263" "ID273"

Preparing Data for Feature Pre-Selection

Here we are going to read each of the 4 OMICs data sets and perform some basic filtering and harmonization for further Feature Pre-Selection step. This step is needed in order to avoid the Curse of Dimensionality problem, i.e. we need to reduce dimensions of each OMIC before putting them together into the integrative DIABLO PLS-DA model.

We start with loading expression, methylation, genotype and phenotype data sets for the selected 110 individuals with all 4 OMICs overlapping. Previously those OMICs were cleaned, log-transformed and prepared for integration.

library("matrixStats")
expr<-read.table("Integr_Expr.txt",header=TRUE,row.names=1,check.names=FALSE,sep="\t")
#expr<-as.data.frame(t(expr))
#hist(rowSds(as.matrix(expr)),breaks=100,xlab="SD OF GENE EXPRESSION",
#     main="Histogram of Standard Deviation in Gene Expression")
#expr<-expr[rowSds(as.matrix(expr))>0.4,]
expr[1:5,1:5]
##       TSPAN6     DPM1    SCYL3 C1orf112       FGR
## ID1 2.768135 2.773802 2.571244 2.129738 0.0000000
## ID3 2.685229 2.706376 2.341311 1.965268 0.8042363
## ID4 2.615901 2.674763 2.485935 2.133980 0.6303739
## ID6 2.530842 3.165768 2.256498 1.634036 0.7873765
## ID8 2.309199 2.574295 2.422172 1.855885 1.0770890
dim(expr)
## [1]   110 18023

Now let us read the matrix of methylation levels and have a look at the data.

library("data.table")
meth<-suppressWarnings(as.data.frame(fread("Integr_Meth.txt")))
rownames(meth)<-meth$V1
meth$V1<-NULL
#meth<-as.data.frame(t(meth))
#hist(rowSds(as.matrix(meth)),breaks=100,xlab="SD OF METHYLATION",
#     main="Histogram of Standard Deviation in Methylation")
#meth<-meth[rowSds(as.matrix(meth))>0.05,]
meth[1:5,1:5]
##     cg00000029 cg00000103 cg00000109 cg00000155 cg00000158
## ID1  0.8889442  0.9751705   1.137247   1.177708  1.0336774
## ID3  0.9016978  0.9840341   1.165657   1.180354  1.0069894
## ID4  0.8936279  0.9910230   1.155727   1.159165  0.9331931
## ID6  0.9557224  0.9558605   1.149778   1.193278  0.9260803
## ID8  0.9145566  0.9641449   1.167043   1.196637  0.9294772
dim(meth)
## [1]    110 816790

Next, we will read the matrix of GWAS genetic variants:

gen<-read.delim("Integr_Gen.txt",header=TRUE,sep="\t",row.names=1,check.names=FALSE)
#gen<-as.data.frame(t(gen))
gen[1:5,1:5]
##     rs1851946_A rs2455144_A rs2455137_G rs2500278_G rs12031275_G
## ID1           0           0           0           0            2
## ID3           1           2           2           2            0
## ID4           2           0           0           0            2
## ID6           0           0           0           0            0
## ID8           2           1           1           1            0
dim(gen)
## [1]  110 2439

And finally let us read phenotypic data:

phen<-read.delim("Integr_Phen.txt",header=TRUE,sep="\t",row.names=1,check.names=FALSE)
#phen<-as.data.frame(t(phen))
phen[1:4,1:4]
##          Age Sex      BMI        SI
## ID1 1.838849   1 1.409933 1.0253059
## ID3 1.662758   0 1.396199 0.6627578
## ID4 1.792392   0 1.457882 1.2013971
## ID6 1.832509   1 1.468347 0.4623980
dim(phen)
## [1] 110   4

We will also read the vector of T2D status for the selected 110 individuals, we will use this for supervision of the DIABLO integration.

T2D<-read.delim("Integr_T2D.txt",header=TRUE,row.names=1,check.names=FALSE,sep="\t")
head(T2D)
##      T2D
## ID1    0
## ID3    0
## ID4    0
## ID6    0
## ID8    0
## ID10   0
dim(T2D)
## [1] 110   1

It is useful to display the Venn Diagram of overlapping samples of the full OMICs data sets before doing integrative analysis:

library("VennDiagram")
## Loading required package: grid
## Loading required package: futile.logger
v<-venn.diagram(list(expr=rownames(expr), phen=rownames(phen), meth=rownames(meth), gen=rownames(gen)),fill = c("orange", "blue","red","green"),alpha = c(0.5, 0.5, 0.5, 0.5), cat.cex = 1.5, cex=1.5, filename=NULL)
grid.newpage()
grid.draw(v)

We can see that the overlap between all 4 OMICs is 110 samples.

DIABLO OMICS Integration

Now we will start integrating the three OMICs: 1) gene expression, 2) methylation and 3) clinical phenotypes. For this purpose we will concatenate gene expression, methylation and phenotype matrices into X matrix and use the T2D status as Y variable, so it is a typical Machine Learning setup: y=f(x), where x is the input, y is the class labels of individuals and the f-function is learnt from the data. We will be using DIABLO model from the mixOmics R packages (Kim-Anh Le Kao is the leader of the project), that is based on multi-block PLS model. To avoid the Curse of Dimensionality, we will pre-select features before integrating them. We build and train the integrative OMICs DIABLO model (as well as feature pre-selection) using train data set (80% of data) and make predictions on a test data set (20% of data).

k<-1
library("mixOmics")
## Loading required package: MASS
## Loading required package: lattice
## Loading required package: ggplot2
## 
## Loaded mixOmics 6.8.5
## Thank you for using mixOmics!
## Tutorials: http://mixomics.org
## Bookdown vignette: https://mixomicsteam.github.io/Bookdown
## Questions, issues: Follow the prompts at http://mixomics.org/contact-us
## Cite us:  citation('mixOmics')
set.seed(k+100)
test_samples<-selected_ind[sample(1:length(selected_ind),round(length(selected_ind)*0.2))]
train_samples<-selected_ind[!selected_ind%in%test_samples]
  
Y.train<-as.factor(as.character(T2D[match(train_samples,rownames(T2D)),]))
Y.test<-as.factor(as.character(T2D[match(test_samples,rownames(T2D)),]))
  
X.train_expr<-expr[match(train_samples,rownames(expr)),]
X.test_expr<-expr[match(test_samples,rownames(expr)),]
expr_plsda<-plsda(X.train_expr, Y.train, ncomp=2)
features_expr1<-names(head(sort(abs(expr_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
features_expr2<-names(head(sort(abs(expr_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
X.train_expr_selected_features<-subset(X.train_expr,select=unique(c(features_expr1, features_expr2)))
X.test_expr_selected_features<-subset(X.test_expr,select=unique(c(features_expr1, features_expr2)))
  
X.train_meth<-meth[match(train_samples,rownames(meth)),]
X.test_meth<-meth[match(test_samples,rownames(meth)),]
meth_plsda<-plsda(X.train_meth, Y.train, ncomp=2)
features_meth1<-names(head(sort(abs(meth_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
features_meth2<-names(head(sort(abs(meth_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
X.train_meth_selected_features<-subset(X.train_meth,select=unique(c(features_meth1, features_meth2)))
X.test_meth_selected_features<-subset(X.test_meth,select=unique(c(features_meth1, features_meth2)))
  
X.train_gen<-gen[match(train_samples,rownames(gen)),]
X.test_gen<-gen[match(test_samples,rownames(gen)),]
gen_plsda<-plsda(X.train_gen, Y.train, ncomp=2)
features_gen1<-names(head(sort(abs(gen_plsda$loadings$X[,"comp1"]),decreasing=TRUE),20))
features_gen2<-names(head(sort(abs(gen_plsda$loadings$X[,"comp2"]),decreasing=TRUE),20))
X.train_gen_selected_features<-subset(X.train_gen,select=unique(c(features_gen1, features_gen2)))
X.test_gen_selected_features<-subset(X.test_gen,select=unique(c(features_gen1, features_gen2)))
  
X.train_phen<-phen[match(train_samples,rownames(phen)),]
X.test_phen<-phen[match(test_samples,rownames(phen)),]
  
data.train<-list(expr=X.train_expr_selected_features, meth=X.train_meth_selected_features, 
                 gen=X.train_gen_selected_features, phen=X.train_phen)
design=matrix(0.1, ncol=length(data.train), nrow=length(data.train), 
              dimnames=list(names(data.train),names(data.train)))
diag(design)=0
design["expr","meth"]<-0.1
design["meth","expr"]<-0.1
design["meth","phen"]<-0.01
design["phen","meth"]<-0.01
design["expr","gen"]<-0.01
design["gen","expr"]<-0.01
design["meth","gen"]<-0.01
design["gen","meth"]<-0.01
  
ncomp=2
list.keepX = list("expr"=c(30,30), "meth"=c(30,30), "gen"=c(10,10), "phen"=c(4,4))
res = block.splsda(X=data.train,Y=Y.train,ncomp=ncomp,keepX=list.keepX,design=design,
                   scheme="horst",mode="regression",init="svd.single",near.zero.var=TRUE)
## Design matrix has changed to include Y; each block will be
##             linked to Y.
plotIndiv(res,legend=TRUE,title="Human Pancreatic Islets: Individual Omics",ellipse=FALSE,ind.names=TRUE,cex=3)

plotArrow(res,ind.names=TRUE,legend=TRUE,title="Human Pancreatic Islets: Consensus Across Omics")

plotVar(res,var.names=TRUE,style='graphics',legend=TRUE,pch=c(16,17,18,19),cex=c(0.8,0.8,0.8,0.8),col=c('blue','red2',"darkgreen","cyan"))

circosPlot(res,cutoff=0.7,line=FALSE,size.variables=0.5)

cimDiablo(res,margins=c(11,18))

network(res,blocks=c(1,2),cex.node.name=0.6,color.node=c('blue','red2'),breaks=NULL)

network(res,blocks=c(1,3),cex.node.name=0.6,color.node=c('blue','darkgreen'),breaks=NULL)

network(res,blocks=c(1,4),cex.node.name=0.6,color.node=c('blue','cyan'),breaks=NULL)

network(res,blocks=c(2,3),cex.node.name=0.6,color.node=c('red2','darkgreen'),breaks=NULL)

network(res,blocks=c(2,4),cex.node.name=0.6,color.node=c('red2','cyan'),breaks=NULL)

network(res,blocks=c(3,4),cex.node.name=0.6,color.node=c('darkgreen','cyan'),breaks=NULL)

data.test<-list(expr=X.test_expr_selected_features, meth=X.test_meth_selected_features, 
                gen=X.test_gen_selected_features, phen=X.test_phen)
predict.diablo=predict(res, newdata=data.test, dist='centroids.dist')
print(data.frame(predict.diablo$class,Truth=Y.test))
##       centroids.dist.expr.comp1 centroids.dist.expr.comp2
## ID227                         0                         0
## ID184                         0                         0
## ID154                         0                         0
## ID168                         1                         1
## ID260                         0                         0
## ID186                         0                         0
## ID221                         1                         1
## ID196                         0                         0
## ID195                         0                         0
## ID189                         0                         0
## ID183                         1                         1
## ID4                           0                         0
## ID97                          0                         0
## ID21                          0                         0
## ID172                         1                         1
## ID91                          0                         0
## ID200                         1                         1
## ID176                         1                         1
## ID194                         1                         1
## ID163                         0                         0
## ID36                          0                         0
## ID241                         1                         1
##       centroids.dist.meth.comp1 centroids.dist.meth.comp2
## ID227                         0                         0
## ID184                         0                         0
## ID154                         0                         0
## ID168                         1                         1
## ID260                         0                         0
## ID186                         0                         0
## ID221                         1                         1
## ID196                         0                         0
## ID195                         0                         0
## ID189                         1                         1
## ID183                         1                         1
## ID4                           0                         0
## ID97                          0                         0
## ID21                          0                         0
## ID172                         1                         1
## ID91                          0                         0
## ID200                         1                         1
## ID176                         1                         0
## ID194                         1                         1
## ID163                         0                         0
## ID36                          0                         0
## ID241                         1                         1
##       centroids.dist.gen.comp1 centroids.dist.gen.comp2
## ID227                        0                        0
## ID184                        1                        1
## ID154                        1                        1
## ID168                        0                        0
## ID260                        0                        0
## ID186                        1                        1
## ID221                        0                        0
## ID196                        0                        1
## ID195                        0                        0
## ID189                        0                        0
## ID183                        0                        0
## ID4                          0                        0
## ID97                         0                        0
## ID21                         0                        0
## ID172                        0                        0
## ID91                         1                        1
## ID200                        0                        1
## ID176                        0                        0
## ID194                        0                        1
## ID163                        0                        1
## ID36                         0                        1
## ID241                        0                        1
##       centroids.dist.phen.comp1 centroids.dist.phen.comp2 Truth
## ID227                         0                         0     0
## ID184                         0                         0     0
## ID154                         1                         1     0
## ID168                         1                         1     1
## ID260                         0                         0     0
## ID186                         0                         0     0
## ID221                         1                         1     1
## ID196                         0                         0     0
## ID195                         0                         1     0
## ID189                         1                         1     0
## ID183                         0                         0     1
## ID4                           0                         0     0
## ID97                          1                         1     0
## ID21                          0                         0     0
## ID172                         0                         0     1
## ID91                          1                         1     1
## ID200                         1                         1     1
## ID176                         1                         0     0
## ID194                         0                         0     1
## ID163                         0                         0     0
## ID36                          1                         1     0
## ID241                         1                         1     1

The network, circus and arrow plots provide an interpretation of the integrative model, i.e. we can see the linkage between the features across the 4 OMICs and potentially can understand the biological interplay between the different layers of information. Let us now display what the integrative model classifier has learnt and let us see how it can classify the new data points from the test data set.

df1<-data.frame(expr=res$variates$expr[,"comp1"],meth=res$variates$meth[,"comp1"],gen=res$variates$gen[,"comp1"],phen=res$variates$phen[,"comp1"])
av1<-rowMeans(df1)
df2<-data.frame(expr=res$variates$expr[,"comp2"],meth=res$variates$meth[,"comp2"],gen=res$variates$gen[,"comp2"],phen=res$variates$phen[,"comp2"])
av2<-rowMeans(df2)

train_df<-data.frame(x=as.numeric(av1),y=as.numeric(av2),label=Y.train)
train_df$color<-ifelse(train_df$label==0,"blue","red")

plot(train_df$x,train_df$y,col=train_df$color,xlab="PLS LATENT DIMENSION 1",ylab="PLS LATENT DIMENSION 2")
legend("topleft", inset=.02, c("Diabetics","Non-Diabetics"), fill=c("red","blue"))
train_df$color<-NULL

mdl <- glm(as.factor(label)~ ., data=train_df, family=binomial)
slope <- coef(mdl)[2]/(-coef(mdl)[3])
intercept <- coef(mdl)[1]/(-coef(mdl)[3]) 
abline(intercept, slope, col="darkorange", lwd=2)
 
 
df1pred<-data.frame(expr=predict.diablo$variates$expr[,"dim1"],meth=predict.diablo$variates$meth[,"dim1"],gen=predict.diablo$variates$gen[,"dim1"],phen=predict.diablo$variates$phen[,"dim1"])
av1pred<-rowMeans(df1pred)
df2pred<-data.frame(expr=predict.diablo$variates$expr[,"dim2"],meth=predict.diablo$variates$meth[,"dim2"],gen=predict.diablo$variates$gen[,"dim2"],phen=predict.diablo$variates$phen[,"dim2"])
av2pred<-rowMeans(df2pred)

test_df<-data.frame(x=as.numeric(av1pred),y=as.numeric(av2pred),label=Y.test)
test_df$color<-ifelse(test_df$label==0,"blue","red")
 
points(test_df$x,test_df$y,col=test_df$color,pch=19)

Now let us calculate some prediction metrics such as the accuracy of prediction, meaning the fraction of times we predict the T2D status correctly:

DIABLO_predict1<-predict.diablo$MajorityVote$centroids.dist[,1]
if(any(is.na(DIABLO_predict1))==TRUE)
{
  failed_samples1<-names(DIABLO_predict1)[is.na(DIABLO_predict1)==TRUE]
  for(s1 in failed_samples1)
  {
    if(as.numeric(predict.diablo$class$centroids.dist$expr[,1][s1])==as.numeric(predict.diablo$class$centroids.dist$meth[,1][s1]))
    {
      DIABLO_predict1[s1]<-predict.diablo$class$centroids.dist$expr[,1][s1]
    }
  }
}
conf_matrix_comp1<-table(DIABLO_predict1,Y.test)
print(conf_matrix_comp1)
##                Y.test
## DIABLO_predict1  0  1
##               0 12  1
##               1  1  7
acc1<-round((sum(diag(conf_matrix_comp1))/sum(conf_matrix_comp1))*100)
print(paste0("Classification Accuracy from DIABLO Component 1: ", acc1))
## [1] "Classification Accuracy from DIABLO Component 1: 90"
DIABLO_predict2<-predict.diablo$MajorityVote$centroids.dist[,2]
if(any(is.na(DIABLO_predict2))==TRUE)
{
  failed_samples2<-names(DIABLO_predict2)[is.na(DIABLO_predict2)==TRUE]
  for(s2 in failed_samples2)
  {
    if(as.numeric(predict.diablo$class$centroids.dist$expr[,2][s2])==as.numeric(predict.diablo$class$centroids.dist$meth[,2][s2]))
    {
      DIABLO_predict2[s2]<-predict.diablo$class$centroids.dist$expr[,2][s2]
    }
  }
}
conf_matrix_comp2<-table(DIABLO_predict2,Y.test)
print(conf_matrix_comp2)
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  0  7
acc2<-round((sum(diag(conf_matrix_comp2))/sum(conf_matrix_comp2))*100)
print(paste0("Classification Accuracy from DIABLO Component 2: ", acc2))
## [1] "Classification Accuracy from DIABLO Component 2: 95"

We can see that the accuracy of prediction is very high, however, since our data set is unbalanced, it is not a very good idea to use accuracy as an ultimate metric of model evaluation. Therefore, let us plot the ROC curve of DIABLO prediction and compare predictions from DIABLO component 1 and component 2:

library("ROCit")

DIABLO_predict1_expr<-predict.diablo$predict$expr[,,1][,2]
DIABLO_predict1_meth<-predict.diablo$predict$meth[,,1][,2]
DIABLO_predict1_gen<-predict.diablo$predict$gen[,,1][,2]
DIABLO_predict1_phen<-predict.diablo$predict$phen[,,1][,2]
DIABLO_predict1_score<-rowMeans(data.frame(DIABLO_predict1_expr,DIABLO_predict1_meth,DIABLO_predict1_gen,DIABLO_predict1_phen))
roc_obj1<-rocit(as.numeric(DIABLO_predict1_score),as.numeric(as.character(Y.test)))
roc_obj1
## $method
## [1] "empirical"
## 
## $pos_count
## [1] 8
## 
## $neg_count
## [1] 14
## 
## $pos_D
## [1] 0.6479727 0.5935829 0.5256018 0.4474015 0.3729831 0.3674957 0.3465465
## [8] 0.3152495
## 
## $neg_D
##  [1]  0.45147708  0.39295108  0.31508390  0.28150240  0.23641488
##  [6]  0.20794347  0.20353990  0.15932217  0.11597300  0.11514357
## [11]  0.10850999  0.07914260  0.02925979 -0.04470402
## 
## $AUC
## [1] 0.9196429
## 
## $Cutoff
##  [1]         Inf  0.64797269  0.59358288  0.52560184  0.45147708
##  [6]  0.44740147  0.39295108  0.37298305  0.36749568  0.34654647
## [11]  0.31524947  0.31508390  0.28150240  0.23641488  0.20794347
## [16]  0.20353990  0.15932217  0.11597300  0.11514357  0.10850999
## [21]  0.07914260  0.02925979 -0.04470402
## 
## $TPR
##  [1] 0.000 0.125 0.250 0.375 0.375 0.500 0.500 0.625 0.750 0.875 1.000
## [12] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## [23] 1.000
## 
## $FPR
##  [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.07142857 0.07142857
##  [7] 0.14285714 0.14285714 0.14285714 0.14285714 0.14285714 0.21428571
## [13] 0.28571429 0.35714286 0.42857143 0.50000000 0.57142857 0.64285714
## [19] 0.71428571 0.78571429 0.85714286 0.92857143 1.00000000
## 
## attr(,"class")
## [1] "rocit"
my_auc1<-0.07142857*0.375+0.07142857*0.5+(1-0.14285714)*1
my_auc1
## [1] 0.9196429
plot(roc_obj1$FPR,roc_obj1$TPR,col="red",type="o",ylab="SENSITIVITY (TPR)",xlab="1-SPECIFISITY (FPR)",pch=19)

DIABLO_predict1_integr<-data.frame(DIABLO_predict1_score,DIABLO_predict1_expr,DIABLO_predict1_meth,DIABLO_predict1_gen,DIABLO_predict1_phen,Y.test)
DIABLO_predict1_integr<-DIABLO_predict1_integr[order(-as.numeric(DIABLO_predict1_integr$DIABLO_predict1_score)),]
DIABLO_predict1_integr
##       DIABLO_predict1_score DIABLO_predict1_expr DIABLO_predict1_meth
## ID200            0.64797269          0.937229783           0.92625879
## ID241            0.59358288          0.653102558           0.81750702
## ID194            0.52560184          0.940421998           0.91060590
## ID176            0.45147708          0.758619817           0.63214077
## ID221            0.44740147          0.578236135           0.65816982
## ID189            0.39295108          0.295121999           0.41269325
## ID168            0.37298305          0.488454189           0.54610304
## ID172            0.36749568          0.710968381           0.44252173
## ID183            0.34654647          0.462599661           0.81986956
## ID91             0.31524947          0.237028387           0.19752766
## ID195            0.31508390          0.380268128           0.30228998
## ID97             0.28150240          0.299570425           0.10517239
## ID154            0.23641488          0.303139657          -0.23289343
## ID196            0.20794347          0.353002091           0.15989229
## ID163            0.20353990          0.197997266           0.03024688
## ID186            0.15932217          0.004421755           0.02623801
## ID184            0.11597300          0.068639148          -0.12447298
## ID227            0.11514357          0.004404258           0.02948682
## ID36             0.10850999         -0.168147125           0.10124112
## ID21             0.07914260          0.145430950           0.01945029
## ID4              0.02925979         -0.276663814          -0.20323961
## ID260           -0.04470402         -0.130024383          -0.20710691
##       DIABLO_predict1_gen DIABLO_predict1_phen Y.test
## ID200          0.20497709           0.52342511      1
## ID241          0.35154259           0.55217936      1
## ID194          0.14132158           0.11005788      1
## ID176          0.10239033           0.31275741      0
## ID221          0.23679453           0.31640538      1
## ID189          0.26749585           0.59649320      0
## ID168         -0.07061965           0.52799463      1
## ID172          0.13818259           0.17831003      1
## ID183         -0.16279651           0.26651315      1
## ID91           0.47005831           0.35638351      1
## ID195          0.30309411           0.27468339      0
## ID97           0.02156155           0.69970523      0
## ID154          0.55988327           0.31553002      0
## ID196          0.30309411           0.01578537      0
## ID163          0.31952207           0.26639340      0
## ID186          0.51956385           0.08706507      0
## ID184          0.42158322           0.09814259      0
## ID227          0.27088340           0.15579979      0
## ID36           0.12108472           0.37986123      0
## ID21          -0.02838194           0.18007112      0
## ID4            0.35243248           0.24451012      0
## ID260         -0.01652360           0.17483882      0
DIABLO_predict2_expr<-predict.diablo$predict$expr[,,2][,2]
DIABLO_predict2_meth<-predict.diablo$predict$meth[,,2][,2]
DIABLO_predict2_gen<-predict.diablo$predict$gen[,,2][,2]
DIABLO_predict2_phen<-predict.diablo$predict$phen[,,2][,2]
DIABLO_predict2_score<-rowMeans(data.frame(DIABLO_predict2_expr,DIABLO_predict2_meth,DIABLO_predict2_gen,DIABLO_predict2_phen))
roc_obj2<-rocit(as.numeric(DIABLO_predict2_score),as.numeric(as.character(Y.test)))
roc_obj2
## $method
## [1] "empirical"
## 
## $pos_count
## [1] 8
## 
## $neg_count
## [1] 14
## 
## $pos_D
## [1] 0.7419619 0.6916058 0.6058110 0.5509729 0.5275448 0.4318585 0.3958000
## [8] 0.3845158
## 
## $neg_D
##  [1]  0.41637059  0.29193374  0.28469600  0.28224560  0.26100506
##  [6]  0.25245874  0.17802293  0.16880295  0.13863866  0.13169311
## [11]  0.09658062  0.08460397  0.07265228 -0.02844850
## 
## $AUC
## [1] 0.9821429
## 
## $Cutoff
##  [1]         Inf  0.74196194  0.69160576  0.60581099  0.55097286
##  [6]  0.52754482  0.43185855  0.41637059  0.39579997  0.38451575
## [11]  0.29193374  0.28469600  0.28224560  0.26100506  0.25245874
## [16]  0.17802293  0.16880295  0.13863866  0.13169311  0.09658062
## [21]  0.08460397  0.07265228 -0.02844850
## 
## $TPR
##  [1] 0.000 0.125 0.250 0.375 0.500 0.625 0.750 0.750 0.875 1.000 1.000
## [12] 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000
## [23] 1.000
## 
## $FPR
##  [1] 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000 0.00000000
##  [7] 0.00000000 0.07142857 0.07142857 0.07142857 0.14285714 0.21428571
## [13] 0.28571429 0.35714286 0.42857143 0.50000000 0.57142857 0.64285714
## [19] 0.71428571 0.78571429 0.85714286 0.92857143 1.00000000
## 
## attr(,"class")
## [1] "rocit"
lines(roc_obj2$FPR,roc_obj2$TPR,col="blue",type="o",pch=19)
lines(c(0,1),c(0,1),col="black")
legend("bottomright",legend=c(paste0("DIABLO COMP1 AUC = ",round(roc_obj1$AUC,2)),paste0("DIABLO COMP2 AUC = ",round(roc_obj2$AUC,2))),col=c("red","blue"),inset=0.02,lty=c(1,1))

DIABLO_predict2_integr<-data.frame(DIABLO_predict2_score,DIABLO_predict2_expr,DIABLO_predict2_meth,DIABLO_predict2_gen,DIABLO_predict2_phen,Y.test)
DIABLO_predict2_integr<-DIABLO_predict2_integr[order(-as.numeric(DIABLO_predict2_integr$DIABLO_predict2_score)),]
DIABLO_predict2_integr
##       DIABLO_predict2_score DIABLO_predict2_expr DIABLO_predict2_meth
## ID200            0.74196194           0.90417965           0.95870973
## ID241            0.69160576           0.86384459           0.87983602
## ID194            0.60581099           1.01220204           0.83748293
## ID221            0.55097286           0.81337243           0.74866557
## ID183            0.52754482           0.85141700           1.04077989
## ID168            0.43185855           0.65705394           0.61925401
## ID189            0.41637059           0.37334054           0.42991535
## ID91             0.39579997           0.40190775           0.33179793
## ID172            0.38451575           0.66021751           0.52546480
## ID97             0.29193374           0.35992054           0.18931742
## ID195            0.28469600           0.34238926           0.14209602
## ID176            0.28224560           0.46175851           0.38959111
## ID154            0.26100506           0.19564166          -0.15740883
## ID36             0.25245874          -0.01614756           0.15328729
## ID186            0.17802293           0.03284843           0.10628508
## ID227            0.16880295           0.08844496           0.04665079
## ID196            0.13863866           0.07165567           0.01971821
## ID163            0.13169311          -0.04228179          -0.11842152
## ID184            0.09658062          -0.01757638          -0.16783628
## ID21             0.08460397          -0.01362324           0.05399999
## ID260            0.07265228          -0.09813842          -0.08621858
## ID4             -0.02844850          -0.27447487          -0.21588235
##       DIABLO_predict2_gen DIABLO_predict2_phen Y.test
## ID200          0.56413495           0.54082341      1
## ID241          0.47192848           0.55081394      1
## ID194          0.47205193           0.10150708      1
## ID221          0.29472760           0.34712584      1
## ID183         -0.06671858           0.28470098      1
## ID168         -0.08864359           0.53976982      1
## ID189          0.25478684           0.60743965      0
## ID91           0.46909908           0.38039514      1
## ID172          0.22068688           0.13169380      1
## ID97          -0.06542497           0.68392198      0
## ID195          0.34848704           0.30581168      0
## ID176         -0.01269149           0.29032429      0
## ID154          0.67925272           0.32653470      0
## ID36           0.47104598           0.40164926      0
## ID186          0.49086806           0.08209016      0
## ID227          0.35100936           0.18910668      0
## ID196          0.41771880           0.04546197      0
## ID163          0.42625542           0.26122034      0
## ID184          0.48631633           0.08541879      0
## ID21           0.12005511           0.17798404      0
## ID260          0.31733767           0.15762844      0
## ID4            0.13276606           0.24379718      0
#roc_obj<-rocit(as.numeric(DIABLO_predict1),as.numeric(as.character(Y.test)))
#plot(roc_obj$FPR,roc_obj$TPR,col="red",type="o",ylab="SENSITIVITY (TPR)",xlab="1-SPECIFISITY (FPR)")
#lines(c(0,1),c(0,1),col="black")
#roc_obj_expr<-rocit(as.numeric(DIABLO_predict1_expr),as.numeric(as.character(Y.test)))
#lines(roc_obj_expr$FPR,roc_obj_expr$TPR,col="blue",type="o")
#roc_obj_meth<-rocit(as.numeric(DIABLO_predict1_meth),as.numeric(as.character(Y.test)))
#lines(roc_obj_meth$FPR,roc_obj_meth$TPR,col="green",type="o")
#roc_obj_gen<-rocit(as.numeric(DIABLO_predict1_gen),as.numeric(as.character(Y.test)))
#lines(roc_obj_gen$FPR,roc_obj_gen$TPR,col="magenta",type="o")
#roc_obj_phen<-rocit(as.numeric(DIABLO_predict1_phen),as.numeric(as.character(Y.test)))
#lines(roc_obj_phen$FPR,roc_obj_phen$TPR,col="cyan",type="o")
#legend("bottomright",legend=c("DIABLO","EXPR","METH","GEN","PHEN"),col=c("red","blue","green","magenta","cyan"),
#       inset=0.02,lty=c(1,1,1,1,1))

We conclude that DIABLO component 2 is more predictive than component 1, that is also confirmed by the higher accuracy of T2D vs. NonT2D classification. The training and evaluation of the model has been performed on one train-test split (so-called holdd-out cross-validartion). Now we are going to do this split multiple times and average the prediction metrics such as accuracy and ROC-curve. In this way we will build confidence intervals for the T2D status prediction.

Building Confidence Intervals for DIABLO Integrative Model

Here we are going to build a loop where we split the 110 individuals into train and test set multiple times (hold-out cross-vlaidation in Machine Learning terminology). Within each split we will run an sPLS-DA model for each OMIC on the train data set only and use the pre-selected features for integrating the 4 OMICs with DIABLO. The results of the integration will be validated multiple times on the test data set in terms of the accuracy of the prediction. In this way we will build confidence intervals for the accuracy of prediction and ROC curve of prediction.

N_repeat<-100
library("mixOmics")
library("ROCit")
library("matrixStats")

comp1_auc<-vector()
comp1_tpr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_fpr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_auc_expr<-vector()
comp1_tpr_expr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_fpr_expr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_auc_meth<-vector()
comp1_tpr_meth<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_fpr_meth<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_auc_gen<-vector()
comp1_tpr_gen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_fpr_gen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_auc_phen<-vector()
comp1_tpr_phen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp1_fpr_phen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)

comp2_auc<-vector()
comp2_tpr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_fpr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_auc_expr<-vector()
comp2_tpr_expr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_fpr_expr<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_auc_meth<-vector()
comp2_tpr_meth<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_fpr_meth<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_auc_gen<-vector()
comp2_tpr_gen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_fpr_gen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_auc_phen<-vector()
comp2_tpr_phen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)
comp2_fpr_phen<-matrix(NA,ncol=length(test_samples)+1,nrow=N_repeat)

comp1_acc<-vector(); comp2_acc<-vector()
for(k in 1:N_repeat)
{
  print(paste0("Working with split No.", k))
  gc()
  set.seed(k+100)
  test_samples<-selected_ind[sample(1:length(selected_ind),round(length(selected_ind)*0.2))]
  train_samples<-selected_ind[!selected_ind%in%test_samples]
  
  Y.train<-as.factor(as.character(T2D[match(train_samples,rownames(T2D)),]))
  Y.test<-as.factor(as.character(T2D[match(test_samples,rownames(T2D)),]))
  
  X.train_expr<-expr[match(train_samples,rownames(expr)),]
  X.test_expr<-expr[match(test_samples,rownames(expr)),]
  expr_plsda<-plsda(X.train_expr, Y.train, ncomp=2)
  features_expr1<-names(head(sort(abs(expr_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
  features_expr2<-names(head(sort(abs(expr_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
  X.train_expr_selected_features<-subset(X.train_expr,select=unique(c(features_expr1, features_expr2)))
  X.test_expr_selected_features<-subset(X.test_expr,select=unique(c(features_expr1, features_expr2)))
  
  X.train_meth<-meth[match(train_samples,rownames(meth)),]
  X.test_meth<-meth[match(test_samples,rownames(meth)),]
  meth_plsda<-plsda(X.train_meth, Y.train, ncomp=2)
  features_meth1<-names(head(sort(abs(meth_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
  features_meth2<-names(head(sort(abs(meth_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
  X.train_meth_selected_features<-subset(X.train_meth,select=unique(c(features_meth1, features_meth2)))
  X.test_meth_selected_features<-subset(X.test_meth,select=unique(c(features_meth1, features_meth2)))
  
  X.train_gen<-gen[match(train_samples,rownames(gen)),]
  X.test_gen<-gen[match(test_samples,rownames(gen)),]
  gen_plsda<-plsda(X.train_gen, Y.train, ncomp=2)
  features_gen1<-names(head(sort(abs(gen_plsda$loadings$X[,"comp1"]),decreasing=TRUE),20))
  features_gen2<-names(head(sort(abs(gen_plsda$loadings$X[,"comp2"]),decreasing=TRUE),20))
    X.train_gen_selected_features<-subset(X.train_gen,select=unique(c(features_gen1, features_gen2)))
  X.test_gen_selected_features<-subset(X.test_gen,select=unique(c(features_gen1, features_gen2)))
  
  X.train_phen<-phen[match(train_samples,rownames(phen)),]
  X.test_phen<-phen[match(test_samples,rownames(phen)),]
  
  data.train<-list(expr=X.train_expr_selected_features, meth=X.train_meth_selected_features, 
                   gen=X.train_gen_selected_features, phen=X.train_phen)
  design=matrix(0.1, ncol=length(data.train), nrow=length(data.train), 
                dimnames=list(names(data.train),names(data.train)))
  diag(design)=0
  design["expr","meth"]<-0.1
  design["meth","expr"]<-0.1
  design["meth","phen"]<-0.01
  design["phen","meth"]<-0.01
  design["expr","gen"]<-0.01
  design["gen","expr"]<-0.01
  design["meth","gen"]<-0.01
  design["gen","meth"]<-0.01
  
  ncomp=2
  list.keepX = list("expr"=c(30,30), "meth"=c(30,30), "gen"=c(5,5), "phen"=c(4,4))
  #tune = tune.block.splsda(X=data.train,Y=Y.train,ncomp=ncomp,test.keepX=list.keepX,design=design,nrepeat=3,folds=2)
  res = block.splsda(X=data.train,Y=Y.train,ncomp=ncomp,keepX=list.keepX,design=design,
                     scheme="horst",mode="regression",init="svd.single",near.zero.var=TRUE)
  
  data.test<-list(expr=X.test_expr_selected_features, meth=X.test_meth_selected_features, 
                  gen=X.test_gen_selected_features, phen=X.test_phen)
  predict.diablo=predict(res, newdata=data.test, dist='centroids.dist')
  #print(data.frame(predict.diablo$class,Truth=Y.test))
  
  DIABLO_predict1_expr<-predict.diablo$predict$expr[,,1][,2]
  DIABLO_predict1_meth<-predict.diablo$predict$meth[,,1][,2]
  DIABLO_predict1_gen<-predict.diablo$predict$gen[,,1][,2]
  DIABLO_predict1_phen<-predict.diablo$predict$phen[,,1][,2]
  DIABLO_predict1_score<-rowWeightedMeans(as.matrix(data.frame(DIABLO_predict1_expr,DIABLO_predict1_meth,
                                                     DIABLO_predict1_gen,DIABLO_predict1_phen)),w=c(1,0.8,0.1,0.1))
  names(DIABLO_predict1_score)<-names(DIABLO_predict1_expr)
  DIABLO_predict1<-predict.diablo$MajorityVote$centroids.dist[,1]
  if(any(is.na(DIABLO_predict1))==TRUE)
  {
    failed_samples1<-names(DIABLO_predict1)[is.na(DIABLO_predict1)==TRUE]
    for(s1 in failed_samples1)
    {
      if(as.numeric(predict.diablo$class$centroids.dist$expr[,1][s1])==as.numeric(predict.diablo$class$centroids.dist$meth[,1][s1]))
      {
        DIABLO_predict1[s1]<-predict.diablo$class$centroids.dist$expr[,1][s1]
      }
      DIABLO_predict1_score[s1]<-predict.diablo$predict$expr[,,1][,2][s1]
    }
    #DIABLO_predict1[failed_samples1]<-predict.diablo$class$centroids.dist$expr[,1][failed_samples1]
  }
  conf_matrix_comp1<-table(DIABLO_predict1,Y.test)
  print(conf_matrix_comp1)
  acc1<-round((sum(diag(conf_matrix_comp1))/sum(conf_matrix_comp1))*100)
  comp1_acc<-append(comp1_acc,acc1)
  print(paste0("Classification Accuracy from PLS Component 1: ", acc1))
  roc_obj1<-rocit(as.numeric(DIABLO_predict1_score),as.numeric(as.character(Y.test)))
  roc_obj1_expr<-rocit(as.numeric(DIABLO_predict1_expr),as.numeric(as.character(Y.test)))
  roc_obj1_meth<-rocit(as.numeric(DIABLO_predict1_meth),as.numeric(as.character(Y.test)))
  roc_obj1_gen<-rocit(as.numeric(DIABLO_predict1_gen),as.numeric(as.character(Y.test)))
  roc_obj1_phen<-rocit(as.numeric(DIABLO_predict1_phen),as.numeric(as.character(Y.test)))
  comp1_auc<-append(comp1_auc,roc_obj1$AUC)
  comp1_auc_expr<-append(comp1_auc_expr,roc_obj1_expr$AUC)
  comp1_auc_meth<-append(comp1_auc_meth,roc_obj1_meth$AUC)
  comp1_auc_gen<-append(comp1_auc_gen,roc_obj1_gen$AUC)
  comp1_auc_phen<-append(comp1_auc_phen,roc_obj1_phen$AUC)
  print(paste0("Classification ROC AUC from DIABLO Component 1: ", roc_obj1$AUC))
  print(paste0("Classification ROC AUC from Expression Component 1: ", roc_obj1_expr$AUC))
  print(paste0("Classification ROC AUC from Methylation Component 1: ", roc_obj1_meth$AUC))
  print(paste0("Classification ROC AUC from Genotype Component 1: ", roc_obj1_gen$AUC))
  print(paste0("Classification ROC AUC from Phenotype Component 1: ", roc_obj1_phen$AUC))
  comp1_tpr[k,]<-roc_obj1$TPR
  comp1_fpr[k,]<-roc_obj1$FPR
  comp1_tpr_expr[k,]<-roc_obj1_expr$TPR
  comp1_fpr_expr[k,]<-roc_obj1_expr$FPR
  comp1_tpr_meth[k,]<-roc_obj1_meth$TPR
  comp1_fpr_meth[k,]<-roc_obj1_meth$FPR
  comp1_tpr_gen[k,]<-roc_obj1_gen$TPR
  comp1_fpr_gen[k,]<-roc_obj1_gen$FPR
  comp1_tpr_phen[k,]<-roc_obj1_phen$TPR
  comp1_fpr_phen[k,]<-roc_obj1_phen$FPR
  
  DIABLO_predict2_expr<-predict.diablo$predict$expr[,,2][,2]
  DIABLO_predict2_meth<-predict.diablo$predict$meth[,,2][,2]
  DIABLO_predict2_gen<-predict.diablo$predict$gen[,,2][,2]
  DIABLO_predict2_phen<-predict.diablo$predict$phen[,,2][,2]
  DIABLO_predict2_score<-rowWeightedMeans(as.matrix(data.frame(DIABLO_predict2_expr,DIABLO_predict2_meth,
                                                     DIABLO_predict2_gen,DIABLO_predict2_phen)),w=c(1,0.8,0.1,0.1))
  names(DIABLO_predict2_score)<-names(DIABLO_predict2_expr)
  DIABLO_predict2<-predict.diablo$MajorityVote$centroids.dist[,2]
  if(any(is.na(DIABLO_predict2))==TRUE)
  {
    failed_samples2<-names(DIABLO_predict2)[is.na(DIABLO_predict2)==TRUE]
    for(s2 in failed_samples2)
    {
      if(as.numeric(predict.diablo$class$centroids.dist$expr[,2][s2])==as.numeric(predict.diablo$class$centroids.dist$meth[,2][s2]))
      {
        DIABLO_predict2[s2]<-predict.diablo$class$centroids.dist$expr[,2][s2]
      }
      DIABLO_predict2_score[s2]<-predict.diablo$predict$expr[,,2][,2][s2]
    }
    #DIABLO_predict2[failed_samples2]<-predict.diablo$class$centroids.dist$expr[,2][failed_samples2]
  }
  conf_matrix_comp2<-table(DIABLO_predict2,Y.test)
  print(conf_matrix_comp2)
  acc2<-round((sum(diag(conf_matrix_comp2))/sum(conf_matrix_comp2))*100)
  comp2_acc<-append(comp2_acc,acc2)
  print(paste0("Classification Accuracy from PLS Component 2: ", acc2))
  roc_obj2<-rocit(as.numeric(DIABLO_predict2_score),as.numeric(as.character(Y.test)))
  roc_obj2_expr<-rocit(as.numeric(DIABLO_predict2_expr),as.numeric(as.character(Y.test)))
  roc_obj2_meth<-rocit(as.numeric(DIABLO_predict2_meth),as.numeric(as.character(Y.test)))
  roc_obj2_gen<-rocit(as.numeric(DIABLO_predict2_gen),as.numeric(as.character(Y.test)))
  roc_obj2_phen<-rocit(as.numeric(DIABLO_predict2_phen),as.numeric(as.character(Y.test)))
  comp2_auc<-append(comp2_auc,roc_obj2$AUC)
  comp2_auc_expr<-append(comp2_auc_expr,roc_obj2_expr$AUC)
  comp2_auc_meth<-append(comp2_auc_meth,roc_obj2_meth$AUC)
  comp2_auc_gen<-append(comp2_auc_gen,roc_obj2_gen$AUC)
  comp2_auc_phen<-append(comp2_auc_phen,roc_obj2_phen$AUC)
  print(paste0("Classification ROC AUC from DIABLO Component 2: ", roc_obj2$AUC))
  print(paste0("Classification ROC AUC from Expression Component 2: ", roc_obj2_expr$AUC))
  print(paste0("Classification ROC AUC from Methylation Component 2: ", roc_obj2_meth$AUC))
  print(paste0("Classification ROC AUC from Genotype Component 2: ", roc_obj2_gen$AUC))
  print(paste0("Classification ROC AUC from Phenotype Component 2: ", roc_obj2_phen$AUC))
  comp2_tpr[k,]<-roc_obj2$TPR
  comp2_fpr[k,]<-roc_obj2$FPR
  comp2_tpr_expr[k,]<-roc_obj2_expr$TPR
  comp2_fpr_expr[k,]<-roc_obj2_expr$FPR
  comp2_tpr_meth[k,]<-roc_obj2_meth$TPR
  comp2_fpr_meth[k,]<-roc_obj2_meth$FPR
  comp2_tpr_gen[k,]<-roc_obj2_gen$TPR
  comp2_fpr_gen[k,]<-roc_obj2_gen$FPR
  comp2_tpr_phen[k,]<-roc_obj2_phen$TPR
  comp2_fpr_phen[k,]<-roc_obj2_phen$FPR
  
  print("***********************************************************")
}
## [1] "Working with split No.1"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  1
##               1  1  7
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.928571428571429"
## [1] "Classification ROC AUC from Expression Component 1: 0.901785714285714"
## [1] "Classification ROC AUC from Methylation Component 1: 0.955357142857143"
## [1] "Classification ROC AUC from Genotype Component 1: 0.473214285714286"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.642857142857143"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  0  7
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Methylation Component 2: 0.982142857142857"
## [1] "Classification ROC AUC from Genotype Component 2: 0.526785714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.642857142857143"
## [1] "***********************************************************"
## [1] "Working with split No.2"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  3
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 81"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.885416666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.895833333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.458333333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.708333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.947916666666667"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.791666666666667"
## [1] "Classification ROC AUC from Genotype Component 2: 0.4375"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.729166666666667"
## [1] "***********************************************************"
## [1] "Working with split No.3"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.888888888888889"
## [1] "Classification ROC AUC from Expression Component 1: 0.948717948717949"
## [1] "Classification ROC AUC from Methylation Component 1: 0.811965811965812"
## [1] "Classification ROC AUC from Genotype Component 1: 0.487179487179487"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.452991452991453"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  3
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.957264957264957"
## [1] "Classification ROC AUC from Expression Component 2: 0.982905982905983"
## [1] "Classification ROC AUC from Methylation Component 2: 0.88034188034188"
## [1] "Classification ROC AUC from Genotype Component 2: 0.478632478632479"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.461538461538462"
## [1] "***********************************************************"
## [1] "Working with split No.4"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  2
##               1  2  3
## [1] "Classification Accuracy from PLS Component 1: 82"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.905882352941176"
## [1] "Classification ROC AUC from Expression Component 1: 0.917647058823529"
## [1] "Classification ROC AUC from Methylation Component 1: 0.858823529411765"
## [1] "Classification ROC AUC from Genotype Component 1: 0.388235294117647"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.470588235294118"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  3
##               1  1  2
## [1] "Classification Accuracy from PLS Component 2: 81"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.905882352941176"
## [1] "Classification ROC AUC from Expression Component 2: 0.894117647058824"
## [1] "Classification ROC AUC from Methylation Component 2: 0.917647058823529"
## [1] "Classification ROC AUC from Genotype Component 2: 0.435294117647059"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.470588235294118"
## [1] "***********************************************************"
## [1] "Working with split No.5"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  3  4
## [1] "Classification Accuracy from PLS Component 1: 76"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.864583333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.833333333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 0.947916666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.479166666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.625"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  2
##               1  1  3
## [1] "Classification Accuracy from PLS Component 2: 83"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.895833333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.833333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.864583333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.645833333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.59375"
## [1] "***********************************************************"
## [1] "Working with split No.6"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  3
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.948717948717949"
## [1] "Classification ROC AUC from Expression Component 1: 0.871794871794872"
## [1] "Classification ROC AUC from Methylation Component 1: 0.923076923076923"
## [1] "Classification ROC AUC from Genotype Component 1: 0.521367521367521"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.487179487179487"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  3
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.931623931623932"
## [1] "Classification ROC AUC from Expression Component 2: 0.905982905982906"
## [1] "Classification ROC AUC from Methylation Component 2: 0.863247863247863"
## [1] "Classification ROC AUC from Genotype Component 2: 0.504273504273504"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.504273504273504"
## [1] "***********************************************************"
## [1] "Working with split No.7"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  1
##               1  2  3
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 0.930555555555556"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.361111111111111"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.541666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  0
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Expression Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.236111111111111"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.541666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.8"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  1
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Expression Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Genotype Component 1: 0.628571428571429"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.6"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  2
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Expression Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Methylation Component 2: 0.952380952380952"
## [1] "Classification ROC AUC from Genotype Component 2: 0.628571428571429"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.6"
## [1] "***********************************************************"
## [1] "Working with split No.9"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  1
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.914285714285714"
## [1] "Classification ROC AUC from Expression Component 1: 0.876190476190476"
## [1] "Classification ROC AUC from Methylation Component 1: 0.904761904761905"
## [1] "Classification ROC AUC from Genotype Component 1: 0.447619047619048"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.580952380952381"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 94"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 2: 0.866666666666667"
## [1] "Classification ROC AUC from Genotype Component 2: 0.504761904761905"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.561904761904762"
## [1] "***********************************************************"
## [1] "Working with split No.10"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  3
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.973214285714286"
## [1] "Classification ROC AUC from Expression Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.892857142857143"
## [1] "Classification ROC AUC from Genotype Component 1: 0.642857142857143"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.598214285714286"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Expression Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Methylation Component 2: 0.973214285714286"
## [1] "Classification ROC AUC from Genotype Component 2: 0.491071428571429"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.580357142857143"
## [1] "***********************************************************"
## [1] "Working with split No.11"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 91"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 0.952941176470588"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.470588235294118"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.635294117647059"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 91"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Expression Component 2: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Genotype Component 2: 0.588235294117647"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.576470588235294"
## [1] "***********************************************************"
## [1] "Working with split No.12"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 10  0
##               1  0  8
## [1] "Classification Accuracy from PLS Component 1: 100"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.933884297520661"
## [1] "Classification ROC AUC from Expression Component 1: 0.917355371900826"
## [1] "Classification ROC AUC from Methylation Component 1: 0.834710743801653"
## [1] "Classification ROC AUC from Genotype Component 1: 0.396694214876033"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.520661157024793"
##                Y.test
## DIABLO_predict2 0 1
##               0 9 1
##               1 0 9
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.983471074380165"
## [1] "Classification ROC AUC from Expression Component 2: 0.983471074380165"
## [1] "Classification ROC AUC from Methylation Component 2: 0.950413223140496"
## [1] "Classification ROC AUC from Genotype Component 2: 0.545454545454546"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.553719008264463"
## [1] "***********************************************************"
## [1] "Working with split No.13"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  5
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 77"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.991666666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.975"
## [1] "Classification ROC AUC from Methylation Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.491666666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.366666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  5
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 77"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.858333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.866666666666667"
## [1] "Classification ROC AUC from Methylation Component 2: 0.85"
## [1] "Classification ROC AUC from Genotype Component 2: 0.416666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.391666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.14"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  0
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.979166666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.96875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.854166666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.302083333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.729166666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.989583333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.96875"
## [1] "Classification ROC AUC from Methylation Component 2: 0.927083333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.208333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.708333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.15"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1 0 1
##               0 8 2
##               1 3 8
## [1] "Classification Accuracy from PLS Component 1: 76"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.867768595041322"
## [1] "Classification ROC AUC from Expression Component 1: 0.851239669421488"
## [1] "Classification ROC AUC from Methylation Component 1: 0.892561983471074"
## [1] "Classification ROC AUC from Genotype Component 1: 0.512396694214876"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.462809917355372"
##                Y.test
## DIABLO_predict2 0 1
##               0 8 2
##               1 1 9
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.917355371900826"
## [1] "Classification ROC AUC from Expression Component 2: 0.925619834710744"
## [1] "Classification ROC AUC from Methylation Component 2: 0.925619834710744"
## [1] "Classification ROC AUC from Genotype Component 2: 0.603305785123967"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.454545454545455"
## [1] "***********************************************************"
## [1] "Working with split No.16"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  3
##               1  0  6
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.958333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.891666666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.891666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.525"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.483333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  2
##               1  0  7
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.975"
## [1] "Classification ROC AUC from Expression Component 2: 0.983333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.95"
## [1] "Classification ROC AUC from Genotype Component 2: 0.583333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.5"
## [1] "***********************************************************"
## [1] "Working with split No.17"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  2
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.930555555555556"
## [1] "Classification ROC AUC from Expression Component 1: 0.875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.972222222222222"
## [1] "Classification ROC AUC from Genotype Component 1: 0.402777777777778"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.680555555555556"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  2
##               1  0  1
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.958333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.958333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.875"
## [1] "Classification ROC AUC from Genotype Component 2: 0.444444444444444"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.680555555555556"
## [1] "***********************************************************"
## [1] "Working with split No.18"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 10  3
##               1  2  5
## [1] "Classification Accuracy from PLS Component 1: 75"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.928571428571429"
## [1] "Classification ROC AUC from Expression Component 1: 0.910714285714286"
## [1] "Classification ROC AUC from Methylation Component 1: 0.991071428571429"
## [1] "Classification ROC AUC from Genotype Component 1: 0.330357142857143"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.571428571428571"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  3
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 82"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.973214285714286"
## [1] "Classification ROC AUC from Expression Component 2: 0.901785714285714"
## [1] "Classification ROC AUC from Methylation Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Genotype Component 2: 0.267857142857143"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.544642857142857"
## [1] "***********************************************************"
## [1] "Working with split No.19"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 100"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 0.988235294117647"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.541176470588235"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.635294117647059"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.388235294117647"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.588235294117647"
## [1] "***********************************************************"
## [1] "Working with split No.20"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  1
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.979166666666667"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 0.947916666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.354166666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.6875"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  2
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.916666666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.9375"
## [1] "Classification ROC AUC from Methylation Component 2: 0.833333333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.3125"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.666666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.21"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  1
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.263157894736842"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.491228070175439"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  1
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.964912280701754"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.175438596491228"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.508771929824561"
## [1] "***********************************************************"
## [1] "Working with split No.22"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  2
##               1  2  3
## [1] "Classification Accuracy from PLS Component 1: 82"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.870588235294118"
## [1] "Classification ROC AUC from Expression Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Methylation Component 1: 0.764705882352941"
## [1] "Classification ROC AUC from Genotype Component 1: 0.482352941176471"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.552941176470588"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  3  3
## [1] "Classification Accuracy from PLS Component 2: 80"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Expression Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Methylation Component 2: 0.929411764705882"
## [1] "Classification ROC AUC from Genotype Component 2: 0.470588235294118"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.564705882352941"
## [1] "***********************************************************"
## [1] "Working with split No.23"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  1
##               1  0  6
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.991071428571429"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 0.973214285714286"
## [1] "Classification ROC AUC from Genotype Component 1: 0.535714285714286"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.508928571428571"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  4
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 82"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.955357142857143"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.446428571428571"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.526785714285714"
## [1] "***********************************************************"
## [1] "Working with split No.24"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.952941176470588"
## [1] "Classification ROC AUC from Expression Component 1: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Genotype Component 1: 0.364705882352941"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.470588235294118"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  1
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.964705882352941"
## [1] "Classification ROC AUC from Expression Component 2: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Genotype Component 2: 0.270588235294118"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.564705882352941"
## [1] "***********************************************************"
## [1] "Working with split No.25"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  1
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.989583333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.96875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.989583333333333"
## [1] "Classification ROC AUC from Genotype Component 1: 0.177083333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.84375"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.947916666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.947916666666667"
## [1] "Classification ROC AUC from Methylation Component 2: 0.78125"
## [1] "Classification ROC AUC from Genotype Component 2: 0.28125"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.854166666666667"
## [1] "***********************************************************"
## [1] "Working with split No.26"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1 0 1
##               0 8 3
##               1 1 7
## [1] "Classification Accuracy from PLS Component 1: 79"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.883333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.825"
## [1] "Classification ROC AUC from Methylation Component 1: 0.941666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.308333333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.541666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 10  3
##               1  1  6
## [1] "Classification Accuracy from PLS Component 2: 80"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.95"
## [1] "Classification ROC AUC from Expression Component 2: 0.95"
## [1] "Classification ROC AUC from Methylation Component 2: 0.966666666666667"
## [1] "Classification ROC AUC from Genotype Component 2: 0.216666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.483333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.27"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  2
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.866666666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.895238095238095"
## [1] "Classification ROC AUC from Methylation Component 1: 0.847619047619048"
## [1] "Classification ROC AUC from Genotype Component 1: 0.39047619047619"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.619047619047619"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  3
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Expression Component 2: 0.980952380952381"
## [1] "Classification ROC AUC from Methylation Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.19047619047619"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.6"
## [1] "***********************************************************"
## [1] "Working with split No.28"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  2
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.888888888888889"
## [1] "Classification ROC AUC from Expression Component 1: 0.875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.819444444444444"
## [1] "Classification ROC AUC from Genotype Component 1: 0.388888888888889"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.763888888888889"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  2
##               1  1  2
## [1] "Classification Accuracy from PLS Component 2: 86"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.833333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.930555555555556"
## [1] "Classification ROC AUC from Methylation Component 2: 0.75"
## [1] "Classification ROC AUC from Genotype Component 2: 0.236111111111111"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.763888888888889"
## [1] "***********************************************************"
## [1] "Working with split No.29"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  0
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.902777777777778"
## [1] "Classification ROC AUC from Expression Component 1: 0.875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.972222222222222"
## [1] "Classification ROC AUC from Genotype Component 1: 0.444444444444444"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.930555555555556"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  1  3
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.944444444444444"
## [1] "Classification ROC AUC from Expression Component 2: 0.944444444444444"
## [1] "Classification ROC AUC from Methylation Component 2: 0.861111111111111"
## [1] "Classification ROC AUC from Genotype Component 2: 0.333333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.888888888888889"
## [1] "***********************************************************"
## [1] "Working with split No.30"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  2
##               1  1  1
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.929411764705882"
## [1] "Classification ROC AUC from Expression Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Methylation Component 1: 0.835294117647059"
## [1] "Classification ROC AUC from Genotype Component 1: 0.517647058823529"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.458823529411765"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  3
##               1  0  1
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.905882352941176"
## [1] "Classification ROC AUC from Expression Component 2: 0.823529411764706"
## [1] "Classification ROC AUC from Methylation Component 2: 0.894117647058824"
## [1] "Classification ROC AUC from Genotype Component 2: 0.435294117647059"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.482352941176471"
## [1] "***********************************************************"
## [1] "Working with split No.31"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  1
##               1  4  6
## [1] "Classification Accuracy from PLS Component 1: 77"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Expression Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.352380952380952"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.457142857142857"
##                Y.test
## DIABLO_predict2 0 1
##               0 8 1
##               1 4 6
## [1] "Classification Accuracy from PLS Component 2: 74"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.352380952380952"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.438095238095238"
## [1] "***********************************************************"
## [1] "Working with split No.32"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  0
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.941176470588235"
## [1] "Classification ROC AUC from Expression Component 1: 0.917647058823529"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.329411764705882"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.858823529411765"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.517647058823529"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.858823529411765"
## [1] "***********************************************************"
## [1] "Working with split No.33"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  0
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.941176470588235"
## [1] "Classification ROC AUC from Expression Component 1: 0.929411764705882"
## [1] "Classification ROC AUC from Methylation Component 1: 0.988235294117647"
## [1] "Classification ROC AUC from Genotype Component 1: 0.552941176470588"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.6"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Expression Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.447058823529412"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.623529411764706"
## [1] "***********************************************************"
## [1] "Working with split No.34"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Expression Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Methylation Component 1: 0.904761904761905"
## [1] "Classification ROC AUC from Genotype Component 1: 0.447619047619048"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.59047619047619"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.952380952380952"
## [1] "Classification ROC AUC from Genotype Component 2: 0.533333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.561904761904762"
## [1] "***********************************************************"
## [1] "Working with split No.35"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 10  4
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 72"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.905982905982906"
## [1] "Classification ROC AUC from Expression Component 1: 0.888888888888889"
## [1] "Classification ROC AUC from Methylation Component 1: 0.871794871794872"
## [1] "Classification ROC AUC from Genotype Component 1: 0.632478632478632"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.615384615384615"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  3
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 86"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.948717948717949"
## [1] "Classification ROC AUC from Expression Component 2: 0.897435897435897"
## [1] "Classification ROC AUC from Methylation Component 2: 0.94017094017094"
## [1] "Classification ROC AUC from Genotype Component 2: 0.547008547008547"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.58974358974359"
## [1] "***********************************************************"
## [1] "Working with split No.36"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 10  1
##               1  1  6
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.948717948717949"
## [1] "Classification ROC AUC from Expression Component 1: 0.957264957264957"
## [1] "Classification ROC AUC from Methylation Component 1: 0.94017094017094"
## [1] "Classification ROC AUC from Genotype Component 1: 0.376068376068376"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.623931623931624"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.982905982905983"
## [1] "Classification ROC AUC from Expression Component 2: 0.982905982905983"
## [1] "Classification ROC AUC from Methylation Component 2: 0.982905982905983"
## [1] "Classification ROC AUC from Genotype Component 2: 0.401709401709402"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.632478632478632"
## [1] "***********************************************************"
## [1] "Working with split No.37"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  3
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 82"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.955357142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.928571428571429"
## [1] "Classification ROC AUC from Genotype Component 1: 0.303571428571429"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.357142857142857"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  2
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.910714285714286"
## [1] "Classification ROC AUC from Expression Component 2: 0.9375"
## [1] "Classification ROC AUC from Methylation Component 2: 0.866071428571429"
## [1] "Classification ROC AUC from Genotype Component 2: 0.339285714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.357142857142857"
## [1] "***********************************************************"
## [1] "Working with split No.38"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  3
##               1  5  2
## [1] "Classification Accuracy from PLS Component 1: 64"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.776470588235294"
## [1] "Classification ROC AUC from Expression Component 1: 0.8"
## [1] "Classification ROC AUC from Methylation Component 1: 0.788235294117647"
## [1] "Classification ROC AUC from Genotype Component 1: 0.364705882352941"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.458823529411765"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  1
##               1  2  3
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.929411764705882"
## [1] "Classification ROC AUC from Expression Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Methylation Component 2: 0.882352941176471"
## [1] "Classification ROC AUC from Genotype Component 2: 0.305882352941176"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.458823529411765"
## [1] "***********************************************************"
## [1] "Working with split No.39"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  1
##               1  0  6
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Expression Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Methylation Component 1: 0.990476190476191"
## [1] "Classification ROC AUC from Genotype Component 1: 0.380952380952381"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.857142857142857"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Methylation Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Genotype Component 2: 0.447619047619048"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.866666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.40"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 91"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.941176470588235"
## [1] "Classification ROC AUC from Expression Component 1: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 1: 0.905882352941176"
## [1] "Classification ROC AUC from Genotype Component 1: 0.423529411764706"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.541176470588235"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  1
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.882352941176471"
## [1] "Classification ROC AUC from Genotype Component 2: 0.529411764705882"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.552941176470588"
## [1] "***********************************************************"
## [1] "Working with split No.41"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  2
##               1  2  2
## [1] "Classification Accuracy from PLS Component 1: 81"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.894117647058824"
## [1] "Classification ROC AUC from Expression Component 1: 0.894117647058824"
## [1] "Classification ROC AUC from Methylation Component 1: 0.823529411764706"
## [1] "Classification ROC AUC from Genotype Component 1: 0.329411764705882"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.776470588235294"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  2
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.882352941176471"
## [1] "Classification ROC AUC from Expression Component 2: 0.905882352941176"
## [1] "Classification ROC AUC from Methylation Component 2: 0.823529411764706"
## [1] "Classification ROC AUC from Genotype Component 2: 0.235294117647059"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.776470588235294"
## [1] "***********************************************************"
## [1] "Working with split No.42"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  1
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.988235294117647"
## [1] "Classification ROC AUC from Expression Component 1: 0.929411764705882"
## [1] "Classification ROC AUC from Methylation Component 1: 0.988235294117647"
## [1] "Classification ROC AUC from Genotype Component 1: 0.317647058823529"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.517647058823529"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 91"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.988235294117647"
## [1] "Classification ROC AUC from Expression Component 2: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 2: 0.988235294117647"
## [1] "Classification ROC AUC from Genotype Component 2: 0.352941176470588"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.505882352941176"
## [1] "***********************************************************"
## [1] "Working with split No.43"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  1
##               1  5  2
## [1] "Classification Accuracy from PLS Component 1: 68"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.912280701754386"
## [1] "Classification ROC AUC from Expression Component 1: 0.929824561403509"
## [1] "Classification ROC AUC from Methylation Component 1: 0.807017543859649"
## [1] "Classification ROC AUC from Genotype Component 1: 0.228070175438596"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.543859649122807"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  1
##               1  3  2
## [1] "Classification Accuracy from PLS Component 2: 82"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.947368421052632"
## [1] "Classification ROC AUC from Expression Component 2: 0.982456140350877"
## [1] "Classification ROC AUC from Methylation Component 2: 0.894736842105263"
## [1] "Classification ROC AUC from Genotype Component 2: 0.140350877192982"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.491228070175439"
## [1] "***********************************************************"
## [1] "Working with split No.44"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1 0 1
##               0 8 1
##               1 2 9
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.942148760330578"
## [1] "Classification ROC AUC from Expression Component 1: 0.933884297520661"
## [1] "Classification ROC AUC from Methylation Component 1: 0.983471074380165"
## [1] "Classification ROC AUC from Genotype Component 1: 0.56198347107438"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.685950413223141"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  1
##               1  0 10
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.991735537190083"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.462809917355372"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.677685950413223"
## [1] "***********************************************************"
## [1] "Working with split No.45"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  0
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.944444444444444"
## [1] "Classification ROC AUC from Expression Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.958333333333333"
## [1] "Classification ROC AUC from Genotype Component 1: 0.402777777777778"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.833333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  0
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.930555555555556"
## [1] "Classification ROC AUC from Genotype Component 2: 0.347222222222222"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.819444444444444"
## [1] "***********************************************************"
## [1] "Working with split No.46"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  2
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.876190476190476"
## [1] "Classification ROC AUC from Expression Component 1: 0.876190476190476"
## [1] "Classification ROC AUC from Methylation Component 1: 0.895238095238095"
## [1] "Classification ROC AUC from Genotype Component 1: 0.438095238095238"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.733333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  2
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.942857142857143"
## [1] "Classification ROC AUC from Genotype Component 2: 0.419047619047619"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.761904761904762"
## [1] "***********************************************************"
## [1] "Working with split No.47"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  2
##               1  2  3
## [1] "Classification Accuracy from PLS Component 1: 80"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.84375"
## [1] "Classification ROC AUC from Expression Component 1: 0.885416666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.8125"
## [1] "Classification ROC AUC from Genotype Component 1: 0.625"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.520833333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.9375"
## [1] "Classification ROC AUC from Expression Component 2: 0.947916666666667"
## [1] "Classification ROC AUC from Methylation Component 2: 0.916666666666667"
## [1] "Classification ROC AUC from Genotype Component 2: 0.572916666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.520833333333333"
## [1] "***********************************************************"
## [1] "Working with split No.48"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  1
##               1  1  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.875"
## [1] "Classification ROC AUC from Expression Component 1: 0.875"
## [1] "Classification ROC AUC from Methylation Component 1: 0.910714285714286"
## [1] "Classification ROC AUC from Genotype Component 1: 0.508928571428571"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.580357142857143"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.526785714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.5625"
## [1] "***********************************************************"
## [1] "Working with split No.49"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  2
##               1  1  8
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.925"
## [1] "Classification ROC AUC from Methylation Component 1: 0.9"
## [1] "Classification ROC AUC from Genotype Component 1: 0.308333333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.75"
##                Y.test
## DIABLO_predict2  0  1
##               0 10  0
##               1  1  9
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.966666666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.975"
## [1] "Classification ROC AUC from Methylation Component 2: 0.983333333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.45"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.7"
## [1] "***********************************************************"
## [1] "Working with split No.50"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  2
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Genotype Component 1: 0.438095238095238"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.523809523809524"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  2
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 86"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Methylation Component 2: 0.857142857142857"
## [1] "Classification ROC AUC from Genotype Component 2: 0.266666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.523809523809524"
## [1] "***********************************************************"
## [1] "Working with split No.51"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  3
##               1  4  1
## [1] "Classification Accuracy from PLS Component 1: 67"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.819444444444444"
## [1] "Classification ROC AUC from Expression Component 1: 0.791666666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.875"
## [1] "Classification ROC AUC from Genotype Component 1: 0.291666666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.625"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  2
##               1  4  2
## [1] "Classification Accuracy from PLS Component 2: 73"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.833333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.861111111111111"
## [1] "Classification ROC AUC from Methylation Component 2: 0.736111111111111"
## [1] "Classification ROC AUC from Genotype Component 2: 0.305555555555556"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.680555555555556"
## [1] "***********************************************************"
## [1] "Working with split No.52"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  2
##               1  1  7
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.931623931623932"
## [1] "Classification ROC AUC from Expression Component 1: 0.923076923076923"
## [1] "Classification ROC AUC from Methylation Component 1: 0.991452991452991"
## [1] "Classification ROC AUC from Genotype Component 1: 0.179487179487179"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.632478632478632"
##                Y.test
## DIABLO_predict2 0 1
##               0 9 1
##               1 1 8
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.974358974358974"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.94017094017094"
## [1] "Classification ROC AUC from Genotype Component 2: 0.136752136752137"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.598290598290598"
## [1] "***********************************************************"
## [1] "Working with split No.53"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.270588235294118"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.658823529411765"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.964705882352941"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.258823529411765"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.635294117647059"
## [1] "***********************************************************"
## [1] "Working with split No.54"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  0
##               1  2  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Expression Component 1: 0.885714285714286"
## [1] "Classification ROC AUC from Methylation Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 1: 0.666666666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.723809523809524"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  1
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Methylation Component 2: 0.980952380952381"
## [1] "Classification ROC AUC from Genotype Component 2: 0.523809523809524"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.733333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.55"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 0.96875"
## [1] "Classification ROC AUC from Genotype Component 1: 0.604166666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.552083333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  2
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.958333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.927083333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.9375"
## [1] "Classification ROC AUC from Genotype Component 2: 0.583333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.53125"
## [1] "***********************************************************"
## [1] "Working with split No.56"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  1
##               1  2  6
## [1] "Classification Accuracy from PLS Component 1: 85"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.919642857142857"
## [1] "Classification ROC AUC from Expression Component 1: 0.928571428571429"
## [1] "Classification ROC AUC from Methylation Component 1: 0.928571428571429"
## [1] "Classification ROC AUC from Genotype Component 1: 0.410714285714286"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.642857142857143"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  2
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.919642857142857"
## [1] "Classification ROC AUC from Expression Component 2: 0.919642857142857"
## [1] "Classification ROC AUC from Methylation Component 2: 0.741071428571429"
## [1] "Classification ROC AUC from Genotype Component 2: 0.383928571428571"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.625"
## [1] "***********************************************************"
## [1] "Working with split No.57"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  1  6
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.919642857142857"
## [1] "Classification ROC AUC from Expression Component 1: 0.883928571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Genotype Component 1: 0.642857142857143"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.4375"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  0
##               1  1  5
## [1] "Classification Accuracy from PLS Component 2: 94"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.964285714285714"
## [1] "Classification ROC AUC from Expression Component 2: 0.955357142857143"
## [1] "Classification ROC AUC from Methylation Component 2: 0.883928571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.473214285714286"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.455357142857143"
## [1] "***********************************************************"
## [1] "Working with split No.58"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  0
##               1  3  4
## [1] "Classification Accuracy from PLS Component 1: 83"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.902777777777778"
## [1] "Classification ROC AUC from Expression Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.944444444444444"
## [1] "Classification ROC AUC from Genotype Component 1: 0.361111111111111"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.708333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  2  3
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Expression Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Methylation Component 2: 0.902777777777778"
## [1] "Classification ROC AUC from Genotype Component 2: 0.333333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.680555555555556"
## [1] "***********************************************************"
## [1] "Working with split No.59"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  2  5
## [1] "Classification Accuracy from PLS Component 1: 81"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.847619047619048"
## [1] "Classification ROC AUC from Expression Component 1: 0.838095238095238"
## [1] "Classification ROC AUC from Methylation Component 1: 0.904761904761905"
## [1] "Classification ROC AUC from Genotype Component 1: 0.219047619047619"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.466666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  0
##               1  2  5
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Methylation Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.19047619047619"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.495238095238095"
## [1] "***********************************************************"
## [1] "Working with split No.60"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  1
##               1  3  1
## [1] "Classification Accuracy from PLS Component 1: 80"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.894736842105263"
## [1] "Classification ROC AUC from Expression Component 1: 0.947368421052632"
## [1] "Classification ROC AUC from Methylation Component 1: 0.771929824561403"
## [1] "Classification ROC AUC from Genotype Component 1: 0.12280701754386"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.508771929824561"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  1
##               1  1  1
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.894736842105263"
## [1] "Classification ROC AUC from Expression Component 2: 0.929824561403509"
## [1] "Classification ROC AUC from Methylation Component 2: 0.789473684210526"
## [1] "Classification ROC AUC from Genotype Component 2: 0.210526315789474"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.543859649122807"
## [1] "***********************************************************"
## [1] "Working with split No.61"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 91"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.964705882352941"
## [1] "Classification ROC AUC from Expression Component 1: 0.941176470588235"
## [1] "Classification ROC AUC from Methylation Component 1: 0.941176470588235"
## [1] "Classification ROC AUC from Genotype Component 1: 0.682352941176471"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.741176470588235"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  1  3
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.917647058823529"
## [1] "Classification ROC AUC from Expression Component 2: 0.905882352941176"
## [1] "Classification ROC AUC from Methylation Component 2: 0.764705882352941"
## [1] "Classification ROC AUC from Genotype Component 2: 0.435294117647059"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.788235294117647"
## [1] "***********************************************************"
## [1] "Working with split No.62"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  0
##               1  2  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 0.989583333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.114583333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.677083333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.1875"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.71875"
## [1] "***********************************************************"
## [1] "Working with split No.63"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  3  3
## [1] "Classification Accuracy from PLS Component 1: 75"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.894117647058824"
## [1] "Classification ROC AUC from Expression Component 1: 0.894117647058824"
## [1] "Classification ROC AUC from Methylation Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Genotype Component 1: 0.364705882352941"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.470588235294118"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  1
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Expression Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Methylation Component 2: 0.941176470588235"
## [1] "Classification ROC AUC from Genotype Component 2: 0.223529411764706"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.470588235294118"
## [1] "***********************************************************"
## [1] "Working with split No.64"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  1
##               1  0  7
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.991071428571429"
## [1] "Classification ROC AUC from Expression Component 1: 0.982142857142857"
## [1] "Classification ROC AUC from Methylation Component 1: 0.964285714285714"
## [1] "Classification ROC AUC from Genotype Component 1: 0.339285714285714"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.678571428571429"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  0  7
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.910714285714286"
## [1] "Classification ROC AUC from Genotype Component 2: 0.294642857142857"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.651785714285714"
## [1] "***********************************************************"
## [1] "Working with split No.65"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  2
##               1  2  2
## [1] "Classification Accuracy from PLS Component 1: 80"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Expression Component 1: 0.858823529411765"
## [1] "Classification ROC AUC from Methylation Component 1: 0.811764705882353"
## [1] "Classification ROC AUC from Genotype Component 1: 0.552941176470588"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.552941176470588"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  1
##               1  1  2
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Expression Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Methylation Component 2: 0.882352941176471"
## [1] "Classification ROC AUC from Genotype Component 2: 0.447058823529412"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.529411764705882"
## [1] "***********************************************************"
## [1] "Working with split No.66"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  4
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 76"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.955357142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.866071428571429"
## [1] "Classification ROC AUC from Genotype Component 1: 0.455357142857143"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.508928571428571"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  3
##               1  1  4
## [1] "Classification Accuracy from PLS Component 2: 80"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.919642857142857"
## [1] "Classification ROC AUC from Expression Component 2: 0.919642857142857"
## [1] "Classification ROC AUC from Methylation Component 2: 0.946428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.544642857142857"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.517857142857143"
## [1] "***********************************************************"
## [1] "Working with split No.67"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  1
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.958333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.958333333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 0.986111111111111"
## [1] "Classification ROC AUC from Genotype Component 1: 0.305555555555556"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.638888888888889"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  1
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Expression Component 2: 0.972222222222222"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.375"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.638888888888889"
## [1] "***********************************************************"
## [1] "Working with split No.68"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  1
##               1  0  6
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.980952380952381"
## [1] "Classification ROC AUC from Expression Component 1: 0.980952380952381"
## [1] "Classification ROC AUC from Methylation Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 1: 0.371428571428571"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.59047619047619"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.428571428571429"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.552380952380952"
## [1] "***********************************************************"
## [1] "Working with split No.69"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  2
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Expression Component 1: 0.916666666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.888888888888889"
## [1] "Classification ROC AUC from Genotype Component 1: 0.125"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.694444444444444"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  0
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.972222222222222"
## [1] "Classification ROC AUC from Genotype Component 2: 0.194444444444444"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.666666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.70"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 17  1
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.972222222222222"
## [1] "Classification ROC AUC from Expression Component 1: 0.972222222222222"
## [1] "Classification ROC AUC from Methylation Component 1: 0.944444444444444"
## [1] "Classification ROC AUC from Genotype Component 1: 0.527777777777778"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.847222222222222"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  0
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.986111111111111"
## [1] "Classification ROC AUC from Genotype Component 2: 0.430555555555556"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.847222222222222"
## [1] "***********************************************************"
## [1] "Working with split No.71"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 100"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.717647058823529"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.647058823529412"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  0
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.905882352941176"
## [1] "Classification ROC AUC from Genotype Component 2: 0.588235294117647"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.611764705882353"
## [1] "***********************************************************"
## [1] "Working with split No.72"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 18  1
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.569444444444444"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.555555555555556"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  0
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.569444444444444"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.527777777777778"
## [1] "***********************************************************"
## [1] "Working with split No.73"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  2
##               1  1  7
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.908333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.858333333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 0.941666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.591666666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.666666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 10  3
##               1  2  7
## [1] "Classification Accuracy from PLS Component 2: 77"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.941666666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.825"
## [1] "Classification ROC AUC from Genotype Component 2: 0.466666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.683333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.74"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 15  2
##               1  2  2
## [1] "Classification Accuracy from PLS Component 1: 81"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.882352941176471"
## [1] "Classification ROC AUC from Expression Component 1: 0.835294117647059"
## [1] "Classification ROC AUC from Methylation Component 1: 0.894117647058824"
## [1] "Classification ROC AUC from Genotype Component 1: 0.364705882352941"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.705882352941176"
##                Y.test
## DIABLO_predict2  0  1
##               0 16  1
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.952941176470588"
## [1] "Classification ROC AUC from Expression Component 2: 0.929411764705882"
## [1] "Classification ROC AUC from Methylation Component 2: 0.988235294117647"
## [1] "Classification ROC AUC from Genotype Component 2: 0.517647058823529"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.658823529411765"
## [1] "***********************************************************"
## [1] "Working with split No.75"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  0
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 94"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Expression Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Methylation Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Genotype Component 1: 0.523809523809524"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.819047619047619"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Expression Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Methylation Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.495238095238095"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.79047619047619"
## [1] "***********************************************************"
## [1] "Working with split No.76"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  0
##               1  2  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Expression Component 1: 0.914285714285714"
## [1] "Classification ROC AUC from Methylation Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 1: 0.695238095238095"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.828571428571429"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  0
##               1  1  6
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.980952380952381"
## [1] "Classification ROC AUC from Methylation Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Genotype Component 2: 0.714285714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.857142857142857"
## [1] "***********************************************************"
## [1] "Working with split No.77"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  2
##               1  0  4
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.980952380952381"
## [1] "Classification ROC AUC from Expression Component 1: 0.980952380952381"
## [1] "Classification ROC AUC from Methylation Component 1: 0.961904761904762"
## [1] "Classification ROC AUC from Genotype Component 1: 0.39047619047619"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.295238095238095"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  1
##               1  1  6
## [1] "Classification Accuracy from PLS Component 2: 91"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Genotype Component 2: 0.647619047619048"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.285714285714286"
## [1] "***********************************************************"
## [1] "Working with split No.78"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  2
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.974358974358974"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 0.965811965811966"
## [1] "Classification ROC AUC from Genotype Component 1: 0.452991452991453"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.760683760683761"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  3
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 86"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.965811965811966"
## [1] "Classification ROC AUC from Genotype Component 2: 0.572649572649573"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.743589743589744"
## [1] "***********************************************************"
## [1] "Working with split No.79"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  4
##               1  0  2
## [1] "Classification Accuracy from PLS Component 1: 79"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.885714285714286"
## [1] "Classification ROC AUC from Methylation Component 1: 0.933333333333333"
## [1] "Classification ROC AUC from Genotype Component 1: 0.419047619047619"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.523809523809524"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  5
##               1  1  2
## [1] "Classification Accuracy from PLS Component 2: 73"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Methylation Component 2: 0.895238095238095"
## [1] "Classification ROC AUC from Genotype Component 2: 0.533333333333333"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.504761904761905"
## [1] "***********************************************************"
## [1] "Working with split No.80"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  2
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 84"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Expression Component 1: 0.885714285714286"
## [1] "Classification ROC AUC from Methylation Component 1: 0.952380952380952"
## [1] "Classification ROC AUC from Genotype Component 1: 0.447619047619048"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.533333333333333"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Expression Component 2: 0.952380952380952"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.609523809523809"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.533333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.81"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  1
##               1  2  3
## [1] "Classification Accuracy from PLS Component 1: 83"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.933333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Methylation Component 1: 0.752380952380952"
## [1] "Classification ROC AUC from Genotype Component 1: 0.514285714285714"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.485714285714286"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  1
##               1  2  5
## [1] "Classification Accuracy from PLS Component 2: 85"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.914285714285714"
## [1] "Classification ROC AUC from Expression Component 2: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 2: 0.904761904761905"
## [1] "Classification ROC AUC from Genotype Component 2: 0.466666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.466666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.82"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  0
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.958333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.930555555555556"
## [1] "Classification ROC AUC from Methylation Component 1: 0.819444444444444"
## [1] "Classification ROC AUC from Genotype Component 1: 0.375"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.666666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 18  0
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.916666666666667"
## [1] "Classification ROC AUC from Genotype Component 2: 0.277777777777778"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.708333333333333"
## [1] "***********************************************************"
## [1] "Working with split No.83"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  0  7
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 0.966666666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.45"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.841666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 12  0
##               1  0  7
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 0.983333333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.366666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.791666666666667"
## [1] "***********************************************************"
## [1] "Working with split No.84"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  0  5
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.989583333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.979166666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.947916666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.552083333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.65625"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 1"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.510416666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.645833333333333"
## [1] "***********************************************************"
## [1] "Working with split No.85"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  3
##               1  2  4
## [1] "Classification Accuracy from PLS Component 1: 76"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.857142857142857"
## [1] "Classification ROC AUC from Expression Component 1: 0.830357142857143"
## [1] "Classification ROC AUC from Methylation Component 1: 0.803571428571429"
## [1] "Classification ROC AUC from Genotype Component 1: 0.428571428571429"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.535714285714286"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  3
##               1  3  3
## [1] "Classification Accuracy from PLS Component 2: 70"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.901785714285714"
## [1] "Classification ROC AUC from Expression Component 2: 0.9375"
## [1] "Classification ROC AUC from Methylation Component 2: 0.821428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.276785714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.544642857142857"
## [1] "***********************************************************"
## [1] "Working with split No.86"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  1
##               1  1  2
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.964912280701754"
## [1] "Classification ROC AUC from Expression Component 1: 0.947368421052632"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.385964912280702"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.368421052631579"
##                Y.test
## DIABLO_predict2  0  1
##               0 19  1
##               1  0  2
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.964912280701754"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.280701754385965"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.403508771929825"
## [1] "***********************************************************"
## [1] "Working with split No.87"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  1
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 89"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Genotype Component 1: 0.533333333333333"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.580952380952381"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  0
##               1  3  5
## [1] "Classification Accuracy from PLS Component 2: 84"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.923809523809524"
## [1] "Classification ROC AUC from Methylation Component 2: 0.914285714285714"
## [1] "Classification ROC AUC from Genotype Component 2: 0.504761904761905"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.552380952380952"
## [1] "***********************************************************"
## [1] "Working with split No.88"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 84"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.980952380952381"
## [1] "Classification ROC AUC from Expression Component 1: 0.971428571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Genotype Component 1: 0.571428571428571"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.514285714285714"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  2
##               1  0  5
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.961904761904762"
## [1] "Classification ROC AUC from Methylation Component 2: 0.971428571428571"
## [1] "Classification ROC AUC from Genotype Component 2: 0.476190476190476"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.523809523809524"
## [1] "***********************************************************"
## [1] "Working with split No.89"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  0
##               1  3  2
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 1"
## [1] "Classification ROC AUC from Expression Component 1: 1"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.225"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.8"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  0
##               1  2  1
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.95"
## [1] "Classification ROC AUC from Expression Component 2: 0.95"
## [1] "Classification ROC AUC from Methylation Component 2: 0.775"
## [1] "Classification ROC AUC from Genotype Component 2: 0.25"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.85"
## [1] "***********************************************************"
## [1] "Working with split No.90"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1 0 1
##               0 9 0
##               1 2 9
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.983333333333333"
## [1] "Classification ROC AUC from Expression Component 1: 0.983333333333333"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.5"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.691666666666667"
##                Y.test
## DIABLO_predict2  0  1
##               0 10  0
##               1  0  9
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.991666666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.991666666666667"
## [1] "Classification ROC AUC from Methylation Component 2: 0.958333333333333"
## [1] "Classification ROC AUC from Genotype Component 2: 0.525"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.725"
## [1] "***********************************************************"
## [1] "Working with split No.91"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  1  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.982142857142857"
## [1] "Classification ROC AUC from Expression Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Methylation Component 1: 0.883928571428571"
## [1] "Classification ROC AUC from Genotype Component 1: 0.455357142857143"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.75"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  1
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.982142857142857"
## [1] "Classification ROC AUC from Expression Component 2: 0.964285714285714"
## [1] "Classification ROC AUC from Methylation Component 2: 0.955357142857143"
## [1] "Classification ROC AUC from Genotype Component 2: 0.401785714285714"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.767857142857143"
## [1] "***********************************************************"
## [1] "Working with split No.92"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 14  0
##               1  1  3
## [1] "Classification Accuracy from PLS Component 1: 94"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.964705882352941"
## [1] "Classification ROC AUC from Expression Component 1: 0.929411764705882"
## [1] "Classification ROC AUC from Methylation Component 1: 0.870588235294118"
## [1] "Classification ROC AUC from Genotype Component 1: 0.658823529411765"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.717647058823529"
##                Y.test
## DIABLO_predict2  0  1
##               0 17  1
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 95"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Expression Component 2: 0.976470588235294"
## [1] "Classification ROC AUC from Methylation Component 2: 0.917647058823529"
## [1] "Classification ROC AUC from Genotype Component 2: 0.470588235294118"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.705882352941177"
## [1] "***********************************************************"
## [1] "Working with split No.93"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 16  2
##               1  0  3
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.96875"
## [1] "Classification ROC AUC from Expression Component 1: 0.979166666666667"
## [1] "Classification ROC AUC from Methylation Component 1: 0.979166666666667"
## [1] "Classification ROC AUC from Genotype Component 1: 0.385416666666667"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.46875"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  2
##               1  0  4
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.947916666666667"
## [1] "Classification ROC AUC from Expression Component 2: 0.927083333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 0.90625"
## [1] "Classification ROC AUC from Genotype Component 2: 0.291666666666667"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.427083333333333"
## [1] "***********************************************************"
## [1] "Working with split No.94"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  1
##               1  2  6
## [1] "Classification Accuracy from PLS Component 1: 86"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.990476190476191"
## [1] "Classification ROC AUC from Expression Component 1: 0.942857142857143"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.514285714285714"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.40952380952381"
##                Y.test
## DIABLO_predict2  0  1
##               0 15  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Expression Component 2: 0.933333333333333"
## [1] "Classification ROC AUC from Methylation Component 2: 1"
## [1] "Classification ROC AUC from Genotype Component 2: 0.6"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.476190476190476"
## [1] "***********************************************************"
## [1] "Working with split No.95"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  3
##               1  1  5
## [1] "Classification Accuracy from PLS Component 1: 81"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.991452991452991"
## [1] "Classification ROC AUC from Expression Component 1: 0.982905982905983"
## [1] "Classification ROC AUC from Methylation Component 1: 0.974358974358974"
## [1] "Classification ROC AUC from Genotype Component 1: 0.427350427350427"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.487179487179487"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  2
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.974358974358974"
## [1] "Classification ROC AUC from Expression Component 2: 0.974358974358974"
## [1] "Classification ROC AUC from Methylation Component 2: 0.931623931623932"
## [1] "Classification ROC AUC from Genotype Component 2: 0.521367521367521"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.478632478632479"
## [1] "***********************************************************"
## [1] "Working with split No.96"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  0
##               1  2  7
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.88034188034188"
## [1] "Classification ROC AUC from Expression Component 1: 0.854700854700855"
## [1] "Classification ROC AUC from Methylation Component 1: 0.905982905982906"
## [1] "Classification ROC AUC from Genotype Component 1: 0.401709401709402"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.726495726495726"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  2
##               1  0  7
## [1] "Classification Accuracy from PLS Component 2: 90"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.931623931623932"
## [1] "Classification ROC AUC from Expression Component 2: 0.923076923076923"
## [1] "Classification ROC AUC from Methylation Component 2: 0.923076923076923"
## [1] "Classification ROC AUC from Genotype Component 2: 0.401709401709402"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.726495726495726"
## [1] "***********************************************************"
## [1] "Working with split No.97"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 13  0
##               1  1  7
## [1] "Classification Accuracy from PLS Component 1: 95"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.955357142857143"
## [1] "Classification ROC AUC from Expression Component 1: 0.9375"
## [1] "Classification ROC AUC from Methylation Component 1: 1"
## [1] "Classification ROC AUC from Genotype Component 1: 0.598214285714286"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.580357142857143"
##                Y.test
## DIABLO_predict2  0  1
##               0 14  0
##               1  0  6
## [1] "Classification Accuracy from PLS Component 2: 100"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Expression Component 2: 0.991071428571429"
## [1] "Classification ROC AUC from Methylation Component 2: 0.955357142857143"
## [1] "Classification ROC AUC from Genotype Component 2: 0.491071428571429"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.571428571428571"
## [1] "***********************************************************"
## [1] "Working with split No.98"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1 0 1
##               0 9 0
##               1 4 7
## [1] "Classification Accuracy from PLS Component 1: 80"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Expression Component 1: 0.923809523809524"
## [1] "Classification ROC AUC from Methylation Component 1: 0.885714285714286"
## [1] "Classification ROC AUC from Genotype Component 1: 0.552380952380952"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.714285714285714"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  0
##               1  2  6
## [1] "Classification Accuracy from PLS Component 2: 89"
## [1] "Classification ROC AUC from DIABLO Component 2: 1"
## [1] "Classification ROC AUC from Expression Component 2: 0.990476190476191"
## [1] "Classification ROC AUC from Methylation Component 2: 0.942857142857143"
## [1] "Classification ROC AUC from Genotype Component 2: 0.552380952380952"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.742857142857143"
## [1] "***********************************************************"
## [1] "Working with split No.99"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 12  2
##               1  0  6
## [1] "Classification Accuracy from PLS Component 1: 90"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.946428571428571"
## [1] "Classification ROC AUC from Expression Component 1: 0.9375"
## [1] "Classification ROC AUC from Methylation Component 1: 0.919642857142857"
## [1] "Classification ROC AUC from Genotype Component 1: 0.357142857142857"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.589285714285714"
##                Y.test
## DIABLO_predict2  0  1
##               0 13  2
##               1  1  6
## [1] "Classification Accuracy from PLS Component 2: 86"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.982142857142857"
## [1] "Classification ROC AUC from Expression Component 2: 0.964285714285714"
## [1] "Classification ROC AUC from Methylation Component 2: 0.973214285714286"
## [1] "Classification ROC AUC from Genotype Component 2: 0.241071428571429"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.642857142857143"
## [1] "***********************************************************"
## [1] "Working with split No.100"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
##                Y.test
## DIABLO_predict1  0  1
##               0 11  3
##               1  1  4
## [1] "Classification Accuracy from PLS Component 1: 79"
## [1] "Classification ROC AUC from DIABLO Component 1: 0.82051282051282"
## [1] "Classification ROC AUC from Expression Component 1: 0.769230769230769"
## [1] "Classification ROC AUC from Methylation Component 1: 0.769230769230769"
## [1] "Classification ROC AUC from Genotype Component 1: 0.478632478632479"
## [1] "Classification ROC AUC from Phenotype Component 1: 0.572649572649573"
##                Y.test
## DIABLO_predict2  0  1
##               0 11  5
##               1  0  3
## [1] "Classification Accuracy from PLS Component 2: 74"
## [1] "Classification ROC AUC from DIABLO Component 2: 0.965811965811966"
## [1] "Classification ROC AUC from Expression Component 2: 0.905982905982906"
## [1] "Classification ROC AUC from Methylation Component 2: 0.863247863247863"
## [1] "Classification ROC AUC from Genotype Component 2: 0.35042735042735"
## [1] "Classification ROC AUC from Phenotype Component 2: 0.547008547008547"
## [1] "***********************************************************"
plot(colMeans(comp1_fpr),colMeans(comp1_tpr),col="red",type="o",ylab="SENSITIVITY (TPR)",xlab="1-SPECIFISITY (FPR)",pch=19)
lines(colMeans(comp1_fpr_expr),colMeans(comp1_tpr_expr),col="blue",type="o",pch=19)
lines(colMeans(comp1_fpr_meth),colMeans(comp1_tpr_meth),col="darkgreen",type="o",pch=19)
lines(colMeans(comp1_fpr_gen),colMeans(comp1_tpr_gen),col="darkorange",type="o",pch=19)
lines(colMeans(comp1_fpr_phen),colMeans(comp1_tpr_phen),col="magenta",type="o",pch=19)
lines(c(0,1),c(0,1),col="black")
legend("bottomright",legend=c(paste0("DIABLO COMP1 AUC = ",round(mean(comp1_auc),2)," +/- ",round(2*sd(comp1_auc),2)),paste0("EXPR COMP1 AUC = ",round(mean(comp1_auc_expr),2)," +/- ",round(2*sd(comp1_auc_expr),2)),paste0("METH COMP1 AUC = ",round(mean(comp1_auc_meth),2)," +/- ",round(2*sd(comp1_auc_meth),2)),paste0("GEN COMP1 AUC = ",round(mean(comp1_auc_gen),2)," +/- ",round(2*sd(comp1_auc_gen),2)),paste0("PHEN COMP1 AUC = ",round(mean(comp1_auc_phen),2)," +/- ",round(2*sd(comp1_auc_phen),2))),col=c("red","blue","darkgreen","darkorange","magenta"),inset=0.02,lty=c(1,1,1,1,1))

plot(colMeans(comp2_fpr),colMeans(comp2_tpr),col="red",type="o",ylab="SENSITIVITY (TPR)",xlab="1-SPECIFISITY (FPR)",pch=19)
lines(colMeans(comp2_fpr_expr),colMeans(comp2_tpr_expr),col="blue",type="o",pch=19)
lines(colMeans(comp2_fpr_meth),colMeans(comp2_tpr_meth),col="darkgreen",type="o",pch=19)
lines(colMeans(comp2_fpr_gen),colMeans(comp2_tpr_gen),col="darkorange",type="o",pch=19)
lines(colMeans(comp2_fpr_phen),colMeans(comp2_tpr_phen),col="magenta",type="o",pch=19)
lines(c(0,1),c(0,1),col="black")
legend("bottomright",legend=c(paste0("DIABLO COMP2 AUC = ",round(mean(comp2_auc),2)," +/- ",round(2*sd(comp2_auc),2)),paste0("EXPR COMP2 AUC = ",round(mean(comp2_auc_expr),2)," +/- ",round(2*sd(comp2_auc_expr),2)),paste0("METH COMP2 AUC = ",round(mean(comp2_auc_meth),2)," +/- ",round(2*sd(comp2_auc_meth),2)),paste0("GEN COMP2 AUC = ",round(mean(comp2_auc_gen),2)," +/- ",round(2*sd(comp2_auc_gen),2)),paste0("PHEN COMP2 AUC = ",round(mean(comp2_auc_phen),2)," +/- ",round(2*sd(comp2_auc_phen),2))),col=c("red","blue","darkgreen","darkorange","magenta"),inset=0.02,lty=c(1,1,1,1,1))

write.table(comp1_auc,file="Comp1_DIABLO_AUC.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp1_tpr,file="Comp1_DIABLO_TPR.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp1_fpr,file="Comp1_DIABLO_FPR.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp2_auc,file="Comp2_DIABLO_AUC.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp2_tpr,file="Comp2_DIABLO_TPR.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp2_fpr,file="Comp2_DIABLO_FPR.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp1_acc,file="Comp1_DIABLO_Acc.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")
write.table(comp2_acc,file="Comp2_DIABLO_Acc.txt",col.names=FALSE,row.names=FALSE,quote=FALSE,sep="\t")

Here we plot the histograms of the DIABLO prediction for components 1 and 2.

gc()
##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   7143622  381.6   13003114  694.5  13003114  694.5
## Vcells 356944289 2723.3  880297686 6716.2 880173343 6715.2
comp1_acc_arch<-as.numeric(scan("Comp1_DIABLO_Acc.txt",what="character"))
comp2_acc_arch<-as.numeric(scan("Comp2_DIABLO_Acc.txt",what="character"))

#comp1_acc_arch<-c(comp1_acc,comp1_acc_arch)
#comp2_acc_arch<-c(comp2_acc,comp2_acc_arch)

hist(comp1_acc_arch,breaks=20,xlab="ACCURACY",main="Accuracy T2D Prediction from DIABLO: PLS1",col="darkgreen",xlim=c(70,100))
abline(v=71,col="red",lwd=5)
mtext(paste0("Accuracy = ",mean(comp1_acc_arch)," +/- ",2*sd(comp1_acc_arch)))

hist(comp2_acc_arch,breaks=20,xlab="ACCURACY",main="Accuracy T2D Prediction from DIABLO: PLS2",col="darkgreen",xlim=c(70,100))
abline(v=71,col="red",lwd=5)
mtext(paste0("Accuracy = ",mean(comp2_acc_arch)," +/- ",2*sd(comp2_acc_arch)))

Now we will access the significance of DIABLO prediction compared to the naive model that predicts every new individual to be a non-diabetic since the NonT2D is the majority class, this naive model would achieve a high accuracy of 71%.

gc()
##             used   (Mb) gc trigger   (Mb)  max used   (Mb)
## Ncells   7145644  381.7   13003114  694.5  13003114  694.5
## Vcells 356948567 2723.4  880297686 6716.2 880173343 6715.2
sum(comp1_acc_arch<=71)/length(comp1_acc_arch)
## [1] 0.03
sum(comp2_acc_arch<=71)/length(comp2_acc_arch)
## [1] 0.01

We conclude that the DIABLO predicts far better than the naive model. Now we will compare the accuracy of DIABLO prediction against the accuracy of predictions from the 4 individual OMICs.

library("RColorBrewer")

my_integr<-as.numeric(scan("Comp2_DIABLO_Acc.txt",what="character"))
my_expr<-as.numeric(scan("Comp2_PLS_Expr_Acc.txt",what="character"))
my_meth<-as.numeric(scan("Comp2_PLS_Meth_Acc.txt",what="character"))
my_gen<-as.numeric(scan("Comp2_PLS_Gen_Acc.txt",what="character"))
my_phen<-as.numeric(scan("Comp2_PLS_Phen_Acc.txt",what="character"))

boxplot(my_integr,my_expr,my_meth,col=brewer.pal(3,"Dark2"),names=c("DIABLO","EXPR","METH"),ylab="T2D PREDICTION ACCURACY",main="Comparison of T2D Prediction between DIABLO and Individual OMICs")

boxplot(my_integr,my_expr,my_meth,my_phen,my_gen,col=brewer.pal(5,"Dark2"),names=c("DIABLO","EXPR","METH","PHEN","GEN"),ylab="T2D PREDICTION ACCURACY",main="Comparison of T2D Prediction between DIABLO and Individual OMICs")

The conclusion we make here is that even though DIABLO marinally otperforms individual OMICs in sense of prediction accuracy, its prediction is largely driven by Expression and Methylation OMICs. So we do not see a dramatic boost in prediction when integrating multiple OMICs. This is probably due to the fact that Genotype and Phenotype OMICs do very poor prediction and puting it together with very predictive Methylation and Expression OMICs only contaminates the analysis.

Now we will rank all the features from all the 4 OMICs by how much they contribute to the final prediction based on multiple train-test splits of the available samples.

expr_features_comp1<-list(); expr_features_comp2<-list()
meth_features_comp1<-list(); meth_features_comp2<-list()
gen_features_comp1<-list(); gen_features_comp2<-list()
phen_features_comp1<-list(); phen_features_comp2<-list()
for(k in 1:N_repeat)
{
  print(paste0("Working with split No.", k))
  gc()
  set.seed(k+100)
  test_samples<-selected_ind[sample(1:length(selected_ind),round(length(selected_ind)*0.2))]
  train_samples<-selected_ind[!selected_ind%in%test_samples]
  
  Y.train<-as.factor(as.character(T2D[match(train_samples,rownames(T2D)),]))
  Y.test<-as.factor(as.character(T2D[match(test_samples,rownames(T2D)),]))
  
  X.train_expr<-expr[match(train_samples,rownames(expr)),]
  X.test_expr<-expr[match(test_samples,rownames(expr)),]
  expr_plsda<-plsda(X.train_expr, Y.train, ncomp=2)
  features_expr1<-names(head(sort(abs(expr_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
  features_expr2<-names(head(sort(abs(expr_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
  X.train_expr_selected_features<-subset(X.train_expr,select=unique(c(features_expr1, features_expr2)))
  X.test_expr_selected_features<-subset(X.test_expr,select=unique(c(features_expr1, features_expr2)))
  
  X.train_meth<-meth[match(train_samples,rownames(meth)),]
  X.test_meth<-meth[match(test_samples,rownames(meth)),]
  meth_plsda<-plsda(X.train_meth, Y.train, ncomp=2)
  features_meth1<-names(head(sort(abs(meth_plsda$loadings$X[,"comp1"]),decreasing=TRUE),50))
  features_meth2<-names(head(sort(abs(meth_plsda$loadings$X[,"comp2"]),decreasing=TRUE),50))
  X.train_meth_selected_features<-subset(X.train_meth,select=unique(c(features_meth1, features_meth2)))
  X.test_meth_selected_features<-subset(X.test_meth,select=unique(c(features_meth1, features_meth2)))
  
  X.train_gen<-gen[match(train_samples,rownames(gen)),]
  X.test_gen<-gen[match(test_samples,rownames(gen)),]
  gen_plsda<-plsda(X.train_gen, Y.train, ncomp=2)
  features_gen1<-names(head(sort(abs(gen_plsda$loadings$X[,"comp1"]),decreasing=TRUE),20))
  features_gen2<-names(head(sort(abs(gen_plsda$loadings$X[,"comp2"]),decreasing=TRUE),20))
  X.train_gen_selected_features<-subset(X.train_gen,select=unique(c(features_gen1, features_gen2)))
  X.test_gen_selected_features<-subset(X.test_gen,select=unique(c(features_gen1, features_gen2)))
  
  X.train_phen<-phen[match(train_samples,rownames(phen)),]
  X.test_phen<-phen[match(test_samples,rownames(phen)),]
  
  data.train<-list(expr=X.train_expr_selected_features, meth=X.train_meth_selected_features, 
                   gen=X.train_gen_selected_features, phen=X.train_phen)
  design=matrix(0.1, ncol=length(data.train), nrow=length(data.train), 
                dimnames=list(names(data.train),names(data.train)))
  diag(design)=0
  design["expr","meth"]<-0.1
  design["meth","expr"]<-0.1
  design["meth","phen"]<-0.01
  design["phen","meth"]<-0.01
  design["expr","gen"]<-0.01
  design["gen","expr"]<-0.01
  design["meth","gen"]<-0.01
  design["gen","meth"]<-0.01
  
  ncomp=2
  list.keepX = list("expr"=c(30,30), "meth"=c(30,30), "gen"=c(5,5), "phen"=c(4,4))
  res = block.splsda(X=data.train,Y=Y.train,ncomp=ncomp,keepX=list.keepX,design=design,
                     scheme="horst",mode="regression",init="svd.single",near.zero.var=TRUE)
  
  expr_features_comp1[[k]]<-as.data.frame(rank(sort(abs(res$loadings$expr[,"comp1"]),decreasing=TRUE)))
  colnames(expr_features_comp1[[k]])<-paste0("iter",k)
  expr_features_comp1[[k]]$GENE<-rownames(expr_features_comp1[[k]])
  expr_features_comp2[[k]]<-as.data.frame(rank(sort(abs(res$loadings$expr[,"comp2"]),decreasing=TRUE)))
  colnames(expr_features_comp2[[k]])<-paste0("iter",k)
  expr_features_comp2[[k]]$GENE<-rownames(expr_features_comp2[[k]])
  
  meth_features_comp1[[k]]<-as.data.frame(rank(sort(abs(res$loadings$meth[,"comp1"]),decreasing=TRUE)))
  colnames(meth_features_comp1[[k]])<-paste0("iter",k)
  meth_features_comp1[[k]]$GENE<-rownames(meth_features_comp1[[k]])
  meth_features_comp2[[k]]<-as.data.frame(rank(sort(abs(res$loadings$meth[,"comp2"]),decreasing=TRUE)))
  colnames(meth_features_comp2[[k]])<-paste0("iter",k)
  meth_features_comp2[[k]]$GENE<-rownames(meth_features_comp2[[k]])
  
  gen_features_comp1[[k]]<-as.data.frame(rank(sort(abs(res$loadings$gen[,"comp1"]),decreasing=TRUE)))
  colnames(gen_features_comp1[[k]])<-paste0("iter",k)
  gen_features_comp1[[k]]$GENE<-rownames(gen_features_comp1[[k]])
  gen_features_comp2[[k]]<-as.data.frame(rank(sort(abs(res$loadings$gen[,"comp2"]),decreasing=TRUE)))
  colnames(gen_features_comp2[[k]])<-paste0("iter",k)
  gen_features_comp2[[k]]$GENE<-rownames(gen_features_comp2[[k]])
  
  phen_features_comp1[[k]]<-as.data.frame(rank(sort(abs(res$loadings$phen[,"comp1"]),decreasing=TRUE)))
  colnames(phen_features_comp1[[k]])<-paste0("iter",k)
  phen_features_comp1[[k]]$GENE<-rownames(phen_features_comp1[[k]])
  phen_features_comp2[[k]]<-as.data.frame(rank(sort(abs(res$loadings$phen[,"comp2"]),decreasing=TRUE)))
  colnames(phen_features_comp2[[k]])<-paste0("iter",k)
  phen_features_comp2[[k]]$GENE<-rownames(phen_features_comp2[[k]])
  
  print("***********************************************************")
}
## [1] "Working with split No.1"
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## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.61"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.62"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.63"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.64"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.65"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.66"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.67"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.68"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.69"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.70"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.71"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.72"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.73"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.74"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.75"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.76"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.77"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.78"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.79"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.80"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.81"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.82"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.83"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.84"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.85"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.86"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.87"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.88"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.89"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.90"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.91"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.92"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.93"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.94"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.95"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.96"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.97"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.98"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.99"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"
## [1] "Working with split No.100"
## Design matrix has changed to include Y; each block will be
##             linked to Y.
## [1] "***********************************************************"

Finally. let us create a resulting list of features for each of the 4 OMICs ranked by their contribution to the T2D predictive model:

expr_features_comp1_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),expr_features_comp1)
rownames(expr_features_comp1_final)<-expr_features_comp1_final$GENE
expr_features_comp1_final$GENE<-NULL
expr_features_comp1_final[is.na(expr_features_comp1_final)]<-0
expr_features_comp1_final$total_rank<-rowSums(expr_features_comp1_final)
expr_features_comp1_final<-expr_features_comp1_final[order(-expr_features_comp1_final$total_rank),]
print(head(expr_features_comp1_final,50))
##          iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## OPRD1       93  92.0  93.0    95  96.0    92  96.0    93  91.0   96.0
## SLC2A2      84  91.0  91.0    94  91.0    90  91.0    92  89.0   85.0
## CHL1        91  90.0  92.0    91  92.0    91  88.0    90  90.0   92.0
## GRAMD2B     90  87.0  89.0    92  95.0    93  90.0    86  87.0   95.0
## FOXE1       92  94.0  94.0    93  93.0    89  87.0    91  92.0   83.0
## ELFN1       87  89.0  90.0    80  84.0    87  83.0    85  88.0   90.0
## GABRA2      88  93.0  88.0    89  94.0    88  84.0    89  86.0   81.0
## ARG2        89  73.0  86.0    82  85.0    81  93.0    87  83.0   91.0
## TFCP2L1     86  88.0  32.5    90  90.0     0  94.0    88  68.0   89.0
## BARX1       77  86.0  84.0    33  72.0    82  33.5    77  74.0   77.0
## CLTRN        0  85.0  83.0    78  73.0    83  85.0    82  82.0   75.0
## PCOLCE2     66  82.0  77.0    86  87.0    76  86.0    83  80.0   88.0
## RASGRP1     81  76.0  79.0    81  83.0    75  78.0    67  63.0   78.0
## PLA1A       68  81.0  66.0    69  69.0    71  74.0    80  73.0   93.0
## COMP        67  77.0  32.5    70  78.0    78  95.0    74  65.0   82.0
## MPP1        70  74.0  87.0    33  88.0    85  81.0     0  76.0   70.0
## GLRA1       74  80.0  75.0    66  89.0    86   0.0    75  84.0    0.0
## GCNT4       83  83.0  32.5    76  33.5    64  33.5    79  75.0   33.5
## HCN4        79  69.0  32.5    75  86.0    32  92.0    32   0.0   84.0
## PRELP        0  84.0  32.5    71  75.0    72  67.0    71  31.5   33.5
## RHOT1       73  32.5  32.5    84  33.5     0  76.0    64  64.0    0.0
## MRO          0  32.5  32.5    73  79.0     0  69.0    81  31.5    0.0
## GAD1        32  67.0  70.0    74  71.0    79  33.5     0  31.5   33.5
## NTN1        71   0.0  81.0    79  77.0     0  89.0     0   0.0   94.0
## DACH2        0   0.0  74.0    72  76.0     0  77.0     0  79.0    0.0
## DCX         32   0.0  71.0    33  33.5    32   0.0    69  85.0    0.0
## ARL4C       32  32.5   0.0    33  68.0    69  33.5    68  31.5   33.5
## TBC1D4       0   0.0  69.0    85  82.0    73  72.0     0   0.0    0.0
## CPXM2        0  75.0  32.5    88  33.5    32   0.0    32  31.5    0.0
## FFAR4       32   0.0  85.0     0  74.0    32   0.0     0   0.0    0.0
## SLC24A2     32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## NOTUM       32  79.0   0.0    33  33.5     0   0.0    32  31.5    0.0
## LRRC2       32   0.0   0.0    33   0.0    32   0.0    65  31.5   74.0
## F11          0  32.5   0.0     0   0.0    77   0.0    72  77.0   33.5
## CMTR2       65  32.5   0.0    33  67.0     0  79.0    84   0.0   33.5
## LSAMP       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## CACNG5      32  32.5   0.0    33   0.0    32  33.5    32  31.5   33.5
## NIPAL4      32  32.5   0.0     0   0.0    70  33.5    32  31.5    0.0
## REEP1       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## TAGLN3      82  65.0  32.5     0   0.0     0  71.0     0   0.0    0.0
## SERPINE2     0  71.0   0.0    83  33.5     0  33.5     0  31.5    0.0
## CLCF1       76  32.5  32.5     0   0.0     0  73.0    32  31.5   76.0
## C1QTNF1      0  32.5  32.5    33  33.5    65  68.0     0   0.0   33.5
## TSKU         0  32.5   0.0    87  80.0    32  33.5     0   0.0   33.5
## KCNA1       32  32.5  32.5    33   0.0     0  33.5    32  31.5   33.5
## SV2B        32  32.5  32.5    33  33.5     0  33.5    32   0.0   33.5
## CA5B        32  72.0  32.5     0   0.0     0   0.0    76  78.0    0.0
## FSTL4       32  32.5  32.5    33   0.0     0  33.5    32  31.5   33.5
## SIX6         0   0.0  68.0    33  81.0    80   0.0     0  31.5    0.0
## DKK3         0  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
##          iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19
## OPRD1      94.0     95     94     93     90     93   94.0     97     96
## SLC2A2     92.0     88     89     92     85     84   96.0     94     94
## CHL1       88.0     92     90     90     87     89   98.0     93     89
## GRAMD2B    90.0     94     87     84     86     87   93.0     95     95
## FOXE1      91.0     90     92     91     91     90   97.0     92     87
## ELFN1      89.0     86     84     89     79     77   92.0     89     97
## GABRA2     93.0     74     95     87     84     92   95.0     86     93
## ARG2       77.0     70     86     77     88     88   91.0     96     77
## TFCP2L1    82.0     91     82     82     82     91   81.0     88     76
## BARX1      85.0     69     88     83     71     32   88.0     69     91
## CLTRN      84.0     77     81     88     80      0   86.0     34     85
## PCOLCE2    83.0     33     83     80     73     32   90.0     87     34
## RASGRP1    86.0     33     70     81     31     78   84.0     90     88
## PLA1A      75.0     84     78     72     67     65   87.0     34     34
## COMP       81.0     71     77     68     68     86   85.0     73     92
## MPP1        0.0      0     93     71     81     82   70.0     82     86
## GLRA1      76.0      0      0     78     70     81   89.0     91      0
## GCNT4      79.0     87      0     79     31     74   34.5     34     82
## HCN4        0.0     68     85     32     89     85    0.0     85      0
## PRELP      78.0      0     33     66      0     32   83.0     34     74
## RHOT1       0.0     76      0     67      0      0   34.5      0     69
## MRO        65.0     83     73     74     69     72    0.0      0      0
## GAD1       32.5     33     80     75     31     32    0.0     34     79
## NTN1        0.0     33     71     32     77     83    0.0     79     34
## DACH2       0.0      0      0     86     78     76    0.0      0      0
## DCX        32.5      0      0     85     83     79   34.5     78      0
## ARL4C      71.0     33     79     32     31     32   74.0     76     90
## TBC1D4      0.0     75      0     70     76      0    0.0      0      0
## CPXM2      72.0     33     33     32     31     32   34.5     34     34
## FFAR4       0.0      0     75      0     66      0   34.5     81     84
## SLC24A2    32.5     33     74     32     31     32   34.5     34     34
## NOTUM      32.5      0      0     32     31      0   34.5     70     34
## LRRC2      32.5     33      0     32     31     73   34.5     68     34
## F11        80.0     85     33     32     31     80   82.0      0     34
## CMTR2       0.0     89      0     32      0     70    0.0     34     34
## LSAMP      32.5     33     33     32     31     32   34.5     34     34
## CACNG5     32.5     33     72     32     31     32   34.5     34      0
## NIPAL4     32.5     33     33     32     31     32   34.5     34     71
## REEP1      32.5     33      0     32      0     32   34.5     34     34
## TAGLN3      0.0      0     67     32     31      0    0.0     77      0
## SERPINE2   87.0      0      0     32      0     68   72.0     34      0
## CLCF1      32.5     72      0      0      0     71   80.0      0     34
## C1QTNF1    67.0      0      0      0      0      0   73.0     72     81
## TSKU        0.0     93     76      0     74     67   34.5      0     80
## KCNA1      32.5      0      0     32     31     32   34.5     34     34
## SV2B       32.5     33      0     32     31      0   34.5     34     34
## CA5B       68.0     33      0      0      0      0    0.0      0     34
## FSTL4      32.5      0     33     32      0     32   34.5     34     34
## SIX6        0.0     33     66     32     31      0   79.0      0     83
## DKK3       32.5      0     33     32      0      0   34.5     34     34
##          iter20 iter21 iter22 iter23 iter24 iter25 iter26 iter27 iter28
## OPRD1      88.0   94.0   94.0     97   96.0   92.0     93     91   91.0
## SLC2A2     90.0   93.0   88.0     96   94.0   89.0     95     93   86.0
## CHL1       87.0   89.0   90.0     93   92.0   86.0     94     90   87.0
## GRAMD2B    84.0   90.0   92.0     91   93.0   87.0     88     87   88.0
## FOXE1      89.0   92.0   93.0     95   95.0   90.0     92     92   90.0
## ELFN1      85.0   91.0   89.0     94   91.0   88.0     90     89   83.0
## GABRA2     86.0   82.0   87.0     92   90.0   91.0     91     84   92.0
## ARG2       83.0   86.0   84.0     90   82.0   81.0     86     85   85.0
## TFCP2L1    66.0   78.0   91.0     89   86.0   82.0     77     83   31.5
## BARX1      82.0   88.0   85.0     87   89.0   85.0     84     82   77.0
## CLTRN      81.0   85.0   83.0     85   84.0   80.0      0     81   82.0
## PCOLCE2    77.0   87.0   32.5     88   83.0   73.0     33     68   72.0
## RASGRP1    79.0   83.0   86.0     83   80.0   84.0     75     86   78.0
## PLA1A      74.0   75.0   80.0     79   88.0   77.0     33      0   89.0
## COMP       62.0   84.0   32.5     34   67.0   83.0     73      0   84.0
## MPP1       75.0   80.0   79.0     69   87.0   70.0     85     79   81.0
## GLRA1      76.0   68.0   76.0     82   81.0   78.0     89     88   79.0
## GCNT4      30.5   71.0   77.0     80    0.0   74.0     33     72   68.0
## HCN4       68.0   32.5   75.0     70   74.0   79.0     69     67   80.0
## PRELP      73.0   74.0    0.0     77   33.5   67.0     78     32   76.0
## RHOT1      30.5   70.0   81.0     84   73.0   31.5      0     32   71.0
## MRO         0.0   32.5   73.0     34   85.0    0.0      0     32   31.5
## GAD1       78.0   32.5   32.5     34   33.5   76.0     33     32   74.0
## NTN1       63.0   79.0   69.0     68   75.0    0.0      0      0    0.0
## DACH2      69.0   77.0   70.0      0    0.0   71.0     79     74    0.0
## DCX        65.0    0.0   82.0      0    0.0   68.0     33     73    0.0
## ARL4C      70.0   81.0   32.5     34   69.0   31.5     72     32   31.5
## TBC1D4     72.0    0.0    0.0      0   79.0    0.0      0     65    0.0
## CPXM2      64.0   32.5   32.5     34   33.5   31.5     33     32   31.5
## FFAR4      71.0   76.0   32.5     72   70.0    0.0     83     70   73.0
## SLC24A2    30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## NOTUM      80.0   32.5   32.5     75   33.5   31.5      0     32   64.0
## LRRC2       0.0   32.5   32.5     34   33.5   64.0      0     32   31.5
## F11         0.0    0.0   72.0      0   33.5   72.0     33     32   31.5
## CMTR2       0.0   32.5   32.5     78    0.0   31.5     33      0   31.5
## LSAMP      30.5   32.5   32.5     34   33.5   31.5     33     32    0.0
## CACNG5     30.5   32.5   32.5     34   33.5   31.5      0     32    0.0
## NIPAL4      0.0   67.0   32.5     34   33.5   31.5     82     32   66.0
## REEP1      30.5   32.5   32.5      0   33.5   31.5     33     32   31.5
## TAGLN3      0.0   32.5   32.5      0   33.5    0.0     66      0    0.0
## SERPINE2   30.5    0.0   32.5     81   33.5    0.0      0     64    0.0
## CLCF1       0.0    0.0   32.5      0   33.5   69.0      0      0   31.5
## C1QTNF1    30.5   66.0    0.0     34   68.0    0.0     81     32   70.0
## TSKU        0.0   65.0    0.0     74   33.5    0.0      0      0    0.0
## KCNA1      30.5   32.5   32.5     34   33.5   31.5      0      0   31.5
## SV2B       30.5   73.0   32.5     34    0.0   31.5      0     32    0.0
## CA5B        0.0    0.0   32.5      0   33.5   75.0      0      0    0.0
## FSTL4      30.5   32.5   32.5      0   33.5   31.5      0      0   31.5
## SIX6       67.0   32.5    0.0      0    0.0   66.0      0      0   75.0
## DKK3       30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
##          iter29 iter30 iter31 iter32 iter33 iter34 iter35 iter36 iter37
## OPRD1        93     95     94   94.0   92.0     88   98.0   96.0     91
## SLC2A2       92     94     91   87.0   89.0     92   97.0   94.0     87
## CHL1         90     91     88   90.0   87.0     91   96.0   91.0     86
## GRAMD2B      87     92     93   91.0   88.0     89   95.0   89.0     89
## FOXE1        91     93     90   93.0   90.0     93   94.0   95.0     92
## ELFN1        88     90     95   89.0   85.0     90   86.0   93.0     90
## GABRA2       89     89     83   85.0   91.0     86   93.0   92.0     93
## ARG2         76     80     85   92.0   78.0     84   91.0   85.0     79
## TFCP2L1      82     79      0   81.0   86.0     85   92.0   86.0     32
## BARX1        80     88     75   75.0   82.0     76   34.5   88.0     88
## CLTRN        84     87     86   73.0   77.0     87   85.0   87.0     78
## PCOLCE2      32     81     87   71.0   76.0     77   88.0   79.0     85
## RASGRP1      81     83      0   79.0   84.0     72    0.0   76.0     84
## PLA1A        69     82     78   72.0   70.0     73   34.5   81.0     74
## COMP         77     33     92   32.5   67.0     32   83.0   75.0     82
## MPP1         66     72      0   84.0    0.0     74   90.0   77.0     77
## GLRA1        71     86      0   83.0   79.0      0   82.0   82.0      0
## GCNT4        85     71     73   70.0   74.0     80    0.0   69.0      0
## HCN4          0      0     81   66.0   75.0     70   80.0   90.0     64
## PRELP        73     85     77   32.5   64.0     32   78.0   33.5     80
## RHOT1         0     75      0   67.0   31.5     75   34.5    0.0      0
## MRO          78     33     74   69.0   83.0     66   34.5   80.0      0
## GAD1         32     33     84   32.5    0.0     69   34.5   33.5     76
## NTN1          0      0      0   65.0   69.0      0   87.0   74.0     70
## DACH2        83      0      0   88.0    0.0     71   75.0    0.0     32
## DCX          64      0     69   86.0   31.5     83    0.0   73.0      0
## ARL4C        32     33     70    0.0   31.5     32   34.5    0.0     65
## TBC1D4        0     84      0   82.0   73.0     82   81.0   78.0      0
## CPXM2        75     33     33   32.5   72.0     32    0.0    0.0     32
## FFAR4         0      0      0   32.5    0.0      0    0.0    0.0     75
## SLC24A2      32     33     33   32.5   31.5     32   34.5   33.5     83
## NOTUM        32     33     33    0.0   31.5     32    0.0   84.0     32
## LRRC2        65     33      0   32.5   31.5     32   34.5   33.5      0
## F11          32     33     67   32.5   31.5      0    0.0    0.0     72
## CMTR2        74     33     33   32.5   81.0     32    0.0   33.5      0
## LSAMP        32     33     33   32.5   31.5     32   34.5   33.5     32
## CACNG5       32     33     33   32.5   31.5     32   34.5   33.5     32
## NIPAL4        0      0     33    0.0    0.0     32    0.0    0.0     69
## REEP1        32     33     33   32.5   31.5     32   34.5   33.5     32
## TAGLN3        0     66      0   32.5    0.0     81   84.0    0.0     32
## SERPINE2     72     33     68    0.0   31.5      0   70.0    0.0     32
## CLCF1        32      0     33   32.5   31.5     32    0.0    0.0      0
## C1QTNF1       0     33     33    0.0    0.0     32    0.0    0.0     68
## TSKU          0     76     66    0.0    0.0      0   74.0   83.0      0
## KCNA1        32      0     33   32.5   31.5     32   34.5   33.5     32
## SV2B         32     33     33   32.5   31.5     32    0.0   33.5     32
## CA5B         86     67     89   32.5   31.5     32    0.0    0.0     32
## FSTL4        32     33     33   32.5   31.5     32   34.5   33.5     32
## SIX6          0      0     33    0.0   31.5      0    0.0    0.0     81
## DKK3          0     33     33    0.0   31.5     32   34.5   33.5     32
##          iter38 iter39 iter40 iter41 iter42 iter43 iter44 iter45 iter46
## OPRD1        97   94.0     89   93.0     93   94.0   94.0     94     97
## SLC2A2       88   92.0     85   91.0     88   93.0   92.0     95     96
## CHL1         95   90.0     86   90.0     90   91.0   88.0     93     88
## GRAMD2B      94   88.0     87   88.0     91   89.0   90.0     91     94
## FOXE1        93   91.0     88   92.0     89   92.0    0.0     92     91
## ELFN1        89   86.0     83   89.0     92   90.0   75.0     88     95
## GABRA2       92   93.0     84   94.0     86   87.0   70.0     86     93
## ARG2         96   74.0     75   70.0     87   88.0   91.0     82     79
## TFCP2L1      87   87.0     73   78.0     82   79.0   93.0     80     92
## BARX1        34   89.0     81   87.0     76   84.0    0.0     83     89
## CLTRN        34   82.0     82   83.0     73   86.0    0.0     85     90
## PCOLCE2      34   83.0     77   85.0     83   66.0   32.5     33     83
## RASGRP1      82   80.0     67   32.5      0   73.0   87.0     78     76
## PLA1A        34   70.0     76   82.0     85   68.0    0.0     89     86
## COMP         70   78.0     74   81.0     84   78.0   32.5     71      0
## MPP1         86   84.0     78   66.0      0   80.0    0.0     84     72
## GLRA1        83   76.0     70   79.0      0   85.0   72.0     81      0
## GCNT4         0   71.0     80   84.0     78   82.0   85.0     90     68
## HCN4         77   81.0      0   32.5      0   83.0   80.0     33     77
## PRELP        81   32.5      0   77.0     68   32.5   78.0     33     71
## RHOT1        69    0.0     79   65.0      0   69.0   73.0     79     34
## MRO           0   73.0      0   71.0      0   32.5    0.0     66     75
## GAD1         34   66.0     71   32.5     32   32.5   32.5     33     34
## NTN1         68   85.0     61   72.0      0    0.0    0.0      0     80
## DACH2        91   75.0      0   74.0      0   81.0    0.0     87      0
## DCX          34   67.0     30    0.0      0   72.0    0.0     33      0
## ARL4C        34   32.5     30   32.5     32   32.5   79.0     33      0
## TBC1D4       90    0.0     72   76.0      0   71.0    0.0      0     81
## CPXM2         0   32.5     30   32.5     32   32.5   32.5     33     87
## FFAR4        75   77.0      0    0.0      0    0.0   32.5      0     34
## SLC24A2      34   32.5     30   32.5     32   32.5   32.5     33     34
## NOTUM         0   32.5      0   32.5     32   32.5   32.5     33      0
## LRRC2         0   32.5     30    0.0     81   32.5   89.0     33     34
## F11           0   65.0     69   32.5     80   32.5    0.0     33      0
## CMTR2         0   32.5     30   32.5     65   67.0   86.0     75      0
## LSAMP        34   32.5     30   32.5     32   32.5   32.5     33     34
## CACNG5       34   32.5     30   32.5     32   32.5   32.5     33     34
## NIPAL4        0   32.5     60    0.0     32    0.0   32.5     33      0
## REEP1        34   32.5     30   32.5     32   32.5    0.0     33     34
## TAGLN3       85    0.0     62    0.0     66   77.0    0.0     70      0
## SERPINE2     34   32.5     63   80.0      0    0.0   65.0      0     78
## CLCF1        34   68.0     65   32.5     32   32.5   71.0     33     34
## C1QTNF1      79    0.0     64    0.0      0   32.5   32.5     33      0
## TSKU         74   32.5      0   69.0      0    0.0    0.0     68     84
## KCNA1         0   32.5     30   32.5      0   32.5   32.5     33     34
## SV2B         34   32.5     30    0.0     32   32.5    0.0     33      0
## CA5B          0   32.5      0   32.5     79   32.5    0.0      0      0
## FSTL4        34   32.5     30   32.5     32   32.5   32.5     33     34
## SIX6          0   32.5      0   86.0      0    0.0    0.0      0      0
## DKK3         34   32.5     30   32.5      0   32.5    0.0     33      0
##          iter47 iter48 iter49 iter50 iter51 iter52 iter53 iter54 iter55
## OPRD1      94.0   93.0   97.0     94   91.0     88     97     98     91
## SLC2A2     89.0   94.0   95.0     95   87.0     89     94     92     88
## CHL1       92.0   92.0   96.0     89   90.0     84     87     82     85
## GRAMD2B    90.0   89.0   94.0     88   86.0     86     95     94     82
## FOXE1      93.0   91.0   98.0     92   88.0     90     93     87     89
## ELFN1      88.0   84.0   87.0     90   89.0     87     96     97     90
## GABRA2     91.0   74.0   86.0     93   92.0     91     88     95     83
## ARG2       86.0   90.0   93.0     77   85.0     85     90     99     79
## TFCP2L1    87.0   86.0   89.0     33   31.5     74     89     93     73
## BARX1      85.0   77.0   79.0     91   83.0     75     85     77     84
## CLTRN      72.0   73.0   71.0     75   79.0     72     78     85     87
## PCOLCE2    78.0   65.0   88.0     79   63.0     76     91     35     78
## RASGRP1    65.0   85.0   90.0     82   81.0     78     92     76     68
## PLA1A      75.0    0.0   77.0     33   84.0      0     83     86     76
## COMP       66.0   32.5   34.5     33   74.0     31     81     81     72
## MPP1       80.0   87.0   80.0     86   64.0     83     86     35     31
## GLRA1      84.0   88.0   85.0     78   82.0     82     77      0     70
## GCNT4      69.0   82.0   34.5      0   78.0      0      0     35     69
## HCN4       73.0   32.5   75.0      0   75.0     71     80     96     81
## PRELP       0.0   32.5   34.5     81   31.5     31     71     35     31
## RHOT1      74.0   75.0   70.0      0   80.0      0      0     78      0
## MRO        77.0    0.0    0.0      0   67.0     31     72     35     86
## GAD1       32.5   32.5    0.0     74   72.0     81     34     35     80
## NTN1       70.0    0.0   82.0     33   31.5     31     79     79     63
## DACH2       0.0   79.0   84.0     83   76.0      0     74      0     74
## DCX        68.0   81.0   92.0     33   69.0      0     34      0     75
## ARL4C      32.5   32.5   34.5     87    0.0      0     34      0     31
## TBC1D4     82.0   78.0    0.0      0    0.0      0      0     91     71
## CPXM2      32.5   32.5   34.5     33   31.5     31     34     35     64
## FFAR4       0.0   68.0   91.0     76    0.0     73     84     89      0
## SLC24A2    32.5   32.5   34.5     68   31.5     68     34     35     31
## NOTUM      32.5   32.5   34.5     67   77.0     31     76     35     77
## LRRC2      32.5   32.5   34.5      0    0.0     31     34     35     31
## F11        76.0   32.5   34.5      0   65.0      0     70      0      0
## CMTR2      32.5    0.0    0.0      0   68.0     31      0     73      0
## LSAMP      32.5   32.5   34.5     33   31.5     31     34     35     31
## CACNG5     32.5   32.5    0.0      0   31.5     31     34     35     31
## NIPAL4     32.5   32.5   73.0     73   31.5     31     69      0      0
## REEP1      32.5   32.5   34.5     33   31.5     31     34     35     31
## TAGLN3      0.0   32.5   76.0     33    0.0      0     68     84      0
## SERPINE2   32.5    0.0    0.0      0    0.0      0     73      0     65
## CLCF1      32.5   32.5   69.0      0   31.5      0      0     88      0
## C1QTNF1     0.0   32.5   34.5     71   31.5      0     34     35      0
## TSKU       83.0   32.5    0.0      0    0.0      0      0     35      0
## KCNA1      32.5   32.5   34.5      0   31.5      0     34     35     31
## SV2B       32.5   32.5   34.5     33   31.5     31     34     35     31
## CA5B       32.5    0.0   34.5     33   31.5     31      0      0     31
## FSTL4      32.5    0.0   34.5     33   31.5      0      0     35     31
## SIX6        0.0    0.0    0.0     85   70.0      0     82      0     31
## DKK3       32.5   32.5   34.5     33   31.5     31     34      0     31
##          iter56 iter57 iter58 iter59 iter60 iter61 iter62 iter63 iter64
## OPRD1        91     89   92.0   94.0   90.0     92   94.0   96.0   93.0
## SLC2A2       90     87   94.0   89.0   91.0     94   93.0   90.0   92.0
## CHL1         88     91   86.0   93.0   89.0     89   91.0   88.0   90.0
## GRAMD2B      83     88   93.0   91.0   84.0     90   87.0   92.0   85.0
## FOXE1        87     90   88.0   90.0   92.0     95   92.0   89.0   91.0
## ELFN1        80     85   89.0   92.0   87.0     88   88.0   93.0   88.0
## GABRA2       89     86   91.0   82.0   88.0     93   90.0   94.0   94.0
## ARG2         86     77   83.0   86.0   86.0     86   89.0   95.0   86.0
## TFCP2L1      75     79   74.0   87.0   72.0     33   83.0   80.0   74.0
## BARX1        82     80   81.0   83.0   81.0     87   86.0   82.0   89.0
## CLTRN        73     75   90.0   81.0   83.0     91   80.0   84.0   32.5
## PCOLCE2      85     83   67.0   71.0   82.0     77   76.0   85.0   77.0
## RASGRP1      79     66   75.0   80.0   31.5     83   82.0   73.0   76.0
## PLA1A        64     69   80.0   84.0   69.0     82   71.0   75.0   82.0
## COMP         71     74   85.0   32.5   31.5     75   74.0   78.0   32.5
## MPP1         66     78   87.0   76.0   85.0     72   84.0   83.0   87.0
## GLRA1        76     82   77.0    0.0   79.0     33   81.0   81.0   79.0
## GCNT4         0     81   68.0   68.0   65.0     84   79.0    0.0    0.0
## HCN4         70      0   84.0   88.0    0.0     33   32.5   87.0   81.0
## PRELP        84     31   65.0   32.5   71.0     33   32.5   33.5    0.0
## RHOT1        31     76   72.0    0.0   78.0     68   32.5   68.0    0.0
## MRO           0      0   76.0    0.0   64.0     79   85.0    0.0   80.0
## GAD1         74     31   32.5   32.5   75.0     81   32.5   33.5   75.0
## NTN1         67      0    0.0   85.0   31.5     33    0.0   72.0   32.5
## DACH2        77      0   71.0   79.0   76.0      0    0.0    0.0    0.0
## DCX          31     31    0.0   32.5   31.5      0   73.0   33.5   70.0
## ARL4C        65     31   32.5    0.0   66.0     70   65.0   33.5   73.0
## TBC1D4        0      0   79.0   75.0   67.0      0   78.0   76.0    0.0
## CPXM2        31     70   32.5   67.0   31.5     33    0.0   33.5   32.5
## FFAR4        63      0   32.5   32.5   77.0      0   72.0   74.0   71.0
## SLC24A2      31     31    0.0   32.5   31.5     33   70.0   33.5   32.5
## NOTUM        78     31   32.5   32.5    0.0     78   68.0   33.5   84.0
## LRRC2        31     31   32.5    0.0   31.5     33   32.5   33.5   32.5
## F11          31     31    0.0    0.0   31.5      0   32.5   33.5    0.0
## CMTR2         0     71   32.5   32.5    0.0     73   32.5    0.0    0.0
## LSAMP        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## CACNG5       31     31    0.0   32.5   31.5     33   32.5   33.5   32.5
## NIPAL4       31     31    0.0   32.5   31.5      0   32.5   33.5   32.5
## REEP1        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## TAGLN3        0      0   70.0   73.0    0.0      0    0.0   69.0    0.0
## SERPINE2     81      0    0.0    0.0   31.5      0    0.0   33.5   66.0
## CLCF1        31     62   32.5   65.0    0.0      0   67.0    0.0   32.5
## C1QTNF1      72     65   69.0    0.0    0.0      0   32.5   67.0   69.0
## TSKU         62     72   32.5   77.0   31.5     69    0.0    0.0    0.0
## KCNA1        31     31   32.5   32.5   31.5     33   32.5    0.0   32.5
## SV2B         31     31   32.5   32.5   31.5     33   32.5    0.0    0.0
## CA5B          0      0    0.0   32.5   70.0     76   77.0    0.0   32.5
## FSTL4         0     31    0.0   32.5    0.0     33   32.5    0.0   32.5
## SIX6         68      0   78.0   32.5   80.0     71    0.0    0.0   67.0
## DKK3         31     31   32.5    0.0   31.5     33   32.5   33.5   32.5
##          iter65 iter66 iter67 iter68 iter69 iter70 iter71 iter72 iter73
## OPRD1      93.0     93     97     92   93.0     97     87   98.0     86
## SLC2A2     89.0     87     91     91   91.0     96     86   96.0     89
## CHL1       92.0     89     92     90   92.0     95     85   93.0     90
## GRAMD2B    91.0     92     95     88   90.0     94     83   95.0     88
## FOXE1      94.0     91     94     93   94.0     93     84   94.0     91
## ELFN1      88.0     86     96     87   88.0     91     82   97.0     85
## GABRA2     90.0     88     93     86   83.0     87     81   84.0     87
## ARG2       79.0     90     85     84   89.0     92     76   85.0     76
## TFCP2L1    86.0     84     87     85   80.0     85     77   83.0     82
## BARX1      83.0     81     79     83   85.0     88     80   89.0     81
## CLTRN      87.0     83     73     80   87.0     90     72   82.0     68
## PCOLCE2    78.0     78     78     82   79.0     86     79   73.0     84
## RASGRP1    81.0     82     89     72   68.0     83     68   77.0     73
## PLA1A      82.0     79     70     73   78.0     89     78   92.0     77
## COMP       32.5     65     88     77   67.0     81     29   90.0     83
## MPP1       70.0     73     84     68   82.0     84     73   87.0     62
## GLRA1      77.0     68     82     69   76.0     70      0    0.0     78
## GCNT4      84.0     70     76     81   73.0      0     75   91.0     67
## HCN4       73.0     85      0     75   81.0     80     69    0.0      0
## PRELP      80.0      0     83     32   32.5     34      0   80.0     75
## RHOT1      32.5     76     34     70   75.0     79     29   86.0      0
## MRO        71.0     75      0     65   32.5     34     74    0.0      0
## GAD1        0.0     32     34     32   32.5     34     71   34.5      0
## NTN1        0.0     80     77      0   70.0     76     66    0.0      0
## DACH2       0.0      0     80     71   84.0     74      0    0.0      0
## DCX        68.0      0     34     89   86.0      0     29   34.5      0
## ARL4C       0.0      0     34     32    0.0     34     29   34.5     71
## TBC1D4     76.0     67      0      0   72.0      0     65   88.0      0
## CPXM2      66.0      0     34     32   32.5     73     29   74.0     31
## FFAR4       0.0      0     34     32   66.0     72      0    0.0      0
## SLC24A2    32.5     32     34     32   32.5     34     29   34.5     31
## NOTUM      32.5     32      0     32   32.5     34     59   34.5     31
## LRRC2      32.5     32     34     32   32.5     34     60   34.5     31
## F11        32.5     32     34     67    0.0     34     63    0.0     65
## CMTR2      67.0     32      0      0    0.0     68      0   81.0      0
## LSAMP      32.5     32     34     32   32.5     34     29   34.5     31
## CACNG5     32.5     32     34     32   32.5     34     29   34.5     31
## NIPAL4     32.5      0     34     32   32.5     75     29   34.5     80
## REEP1      32.5     32      0     32   32.5     34     29   34.5     31
## TAGLN3     75.0     74     90      0    0.0     78      0   78.0     31
## SERPINE2   32.5     32     71     32   32.5      0     29    0.0     70
## CLCF1      32.5      0     34      0   32.5     71      0   69.0     66
## C1QTNF1     0.0      0     69      0    0.0     34      0    0.0     64
## TSKU       65.0     32      0     79   32.5      0      0   79.0      0
## KCNA1      32.5     32     34     32   32.5     34     29   34.5     31
## SV2B       32.5     32     34     32   32.5     34     29   34.5     31
## CA5B       32.5      0      0     32   32.5      0     64    0.0     79
## FSTL4      32.5     32     34     32   32.5     34     29   34.5     31
## SIX6        0.0     32      0      0    0.0      0      0    0.0      0
## DKK3       32.5     32      0     32   32.5     34      0   34.5     31
##          iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## OPRD1      90.0   94.0     94     95   93.0   96.0   93.0     95   94.0
## SLC2A2     88.0   90.0     95     94   92.0   97.0   87.0     91   93.0
## CHL1       85.0   84.0     92     90   91.0   95.0   92.0     92   92.0
## GRAMD2B    86.0   89.0     87     88   89.0   93.0   91.0     88   88.0
## FOXE1      89.0   92.0     91     89   94.0   98.0   90.0     93   91.0
## ELFN1      87.0   91.0     88     91   90.0   90.0   82.0     90   89.0
## GABRA2     80.0   85.0     93     93   87.0   92.0   86.0     94   90.0
## ARG2       71.0   77.0     85     86   86.0   74.0   89.0     87   78.0
## TFCP2L1    82.0   32.5     70     85   74.0   76.0   94.0     81   84.0
## BARX1      81.0   79.0     90     92   88.0   88.0    0.0     86   87.0
## CLTRN      65.0   73.0     78     87   80.0   86.0   32.5     84   72.0
## PCOLCE2    62.0   72.0     79     83   85.0   89.0   83.0     76   76.0
## RASGRP1    78.0   93.0     80     80   72.0   85.0    0.0     73   81.0
## PLA1A      30.5   68.0     86     84   79.0   94.0   32.5     33   32.5
## COMP       77.0   87.0     33     75    0.0   79.0   84.0     74   80.0
## MPP1       30.5    0.0     89     73   75.0   91.0   65.0     82   82.0
## GLRA1      79.0   74.0     82     78   83.0   82.0   32.5     78   83.0
## GCNT4      84.0   75.0     33     77   84.0    0.0   32.5     33   86.0
## HCN4       66.0    0.0     75     81   69.0   72.0   88.0     75   66.0
## PRELP      30.5   67.0     33     74   66.0   83.0   32.5     33   74.0
## RHOT1      30.5   80.0     33      0   32.5    0.0    0.0      0   69.0
## MRO        61.0   32.5     84     79    0.0    0.0    0.0     33   79.0
## GAD1       30.5   32.5     74     33   32.5    0.0   32.5     67   32.5
## NTN1       74.0    0.0     33     76    0.0    0.0   85.0     79   32.5
## DACH2      76.0   78.0     77      0   81.0   34.5    0.0     83   85.0
## DCX        75.0   86.0     33      0    0.0    0.0    0.0     70    0.0
## ARL4C      30.5   81.0     69     33    0.0   75.0   32.5     33   70.0
## TBC1D4     70.0   83.0     73      0    0.0    0.0    0.0     71    0.0
## CPXM2      72.0   32.5     33     33   32.5   34.5    0.0     33   67.0
## FFAR4       0.0   32.5     81     72   76.0   81.0   32.5     85   32.5
## SLC24A2    30.5   32.5     33     33   32.5   34.5   32.5     68   32.5
## NOTUM       0.0    0.0     33     68   70.0   34.5    0.0     33   32.5
## LRRC2      30.5   65.0     33     33   32.5   34.5   32.5     33   32.5
## F11        83.0   32.5      0     33    0.0   34.5   32.5     69    0.0
## CMTR2      63.0    0.0      0     71   32.5    0.0   71.0      0   65.0
## LSAMP      30.5   32.5     33     33   32.5   34.5    0.0     33   32.5
## CACNG5     30.5   32.5      0     33   32.5    0.0   32.5     33   32.5
## NIPAL4     30.5    0.0     33     66    0.0   84.0   32.5      0   32.5
## REEP1      30.5   32.5     33     33   32.5   34.5    0.0     33   32.5
## TAGLN3      0.0    0.0     33     69    0.0   34.5    0.0     80    0.0
## SERPINE2   69.0   82.0      0      0    0.0    0.0   68.0     33   32.5
## CLCF1      30.5   32.5      0      0    0.0    0.0   66.0      0    0.0
## C1QTNF1     0.0   76.0     33     33    0.0   69.0    0.0     33   32.5
## TSKU        0.0   32.5     33     33    0.0    0.0   74.0     33   32.5
## KCNA1      30.5   32.5      0     33   32.5    0.0   32.5     33   32.5
## SV2B       30.5    0.0      0     33   32.5    0.0    0.0     72   32.5
## CA5B       30.5    0.0      0     33   71.0    0.0    0.0      0   32.5
## FSTL4      30.5   32.5     33     33   32.5   34.5    0.0     33   32.5
## SIX6        0.0   32.5     83     70    0.0   77.0    0.0     89    0.0
## DKK3        0.0   32.5     33     33   32.5   34.5    0.0     33   32.5
##          iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## OPRD1      98.0   98.0     92   96.0     91   94.0     93     95   95.0
## SLC2A2     94.0   94.0     90   91.0     89   90.0     92     92   90.0
## CHL1       97.0   92.0     91   94.0     87   92.0     91     79   96.0
## GRAMD2B    95.0   96.0     86   87.0     90   87.0     86     94   92.0
## FOXE1      96.0   97.0     93   92.0     88   91.0     88      0   91.0
## ELFN1      91.0   95.0     88   88.0     93   89.0     90     97   88.0
## GABRA2     93.0   77.0     89   95.0     74   84.0     87     93   94.0
## ARG2       88.0   82.0     84   86.0     86   83.0     82     90   87.0
## TFCP2L1    80.0   91.0     79   93.0      0   86.0     76     84   81.0
## BARX1      72.0   74.0     85   83.0     32   78.0     83     82   78.0
## CLTRN      71.0   90.0     72   89.0     84   88.0     84     86   84.0
## PCOLCE2    84.0   89.0     75   78.0     85   93.0     85     77   83.0
## RASGRP1    77.0   93.0     82   79.0      0   76.0     79      0   93.0
## PLA1A      83.0   78.0     81   90.0     65   73.0     89     91   89.0
## COMP       92.0   73.0     32   71.0     92   85.0     71     89   73.0
## MPP1       76.0    0.0     80   82.0      0   66.0     81      0    0.0
## GLRA1       0.0    0.0     87   75.0      0   74.0     75      0   76.0
## GCNT4      34.5   76.0     64   72.0     70   68.0     65     74   68.0
## HCN4       89.0   70.0     32   84.0      0   82.0     69     80   33.5
## PRELP      34.5   34.5     32   33.5     73   77.0     32     88   79.0
## RHOT1      73.0   84.0     32   68.0     32   75.0     72     73   86.0
## MRO         0.0   87.0     32   85.0      0   81.0     68     70    0.0
## GAD1       34.5   34.5      0   33.5     71   32.5     77     34   80.0
## NTN1       82.0    0.0     32   77.0      0   79.0     64      0    0.0
## DACH2      90.0   80.0     78    0.0      0    0.0      0      0   85.0
## DCX        78.0   88.0     73   69.0     82    0.0      0      0   33.5
## ARL4C      34.5   34.5     32   33.5      0    0.0     70     34   74.0
## TBC1D4     75.0   75.0      0   81.0      0    0.0      0     81    0.0
## CPXM2      34.5   81.0     32    0.0      0   69.0     32     34   33.5
## FFAR4      34.5    0.0     83    0.0      0    0.0     73      0   82.0
## SLC24A2     0.0   34.5     32   33.5     32   32.5     32     34   33.5
## NOTUM       0.0   34.5     68   33.5      0   72.0     32      0   33.5
## LRRC2      34.5   79.0      0   33.5     75   32.5     32     34   33.5
## F11        34.5   34.5     32   33.5     83   32.5      0      0   33.5
## CMTR2       0.0   34.5      0   70.0      0   80.0     32     96   33.5
## LSAMP      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## CACNG5     34.5   34.5      0   33.5     32   32.5     74     34   33.5
## NIPAL4     34.5    0.0     32   33.5     67    0.0     80      0   33.5
## REEP1      34.5   34.5     32   33.5     32    0.0     32     34   33.5
## TAGLN3     69.0   72.0     71   80.0      0   32.5      0      0    0.0
## SERPINE2   34.5   34.5      0    0.0     32   70.0     32      0   70.0
## CLCF1       0.0    0.0      0   33.5     72    0.0      0     78   75.0
## C1QTNF1     0.0   34.5     32    0.0      0    0.0     32     83   72.0
## TSKU       70.0    0.0      0   33.5      0   71.0      0     34    0.0
## KCNA1      34.5   34.5     32   33.5     32   32.5      0     34   33.5
## SV2B        0.0   34.5     32   33.5     32   32.5     32      0    0.0
## CA5B       34.5   34.5     32    0.0     81   32.5     32      0    0.0
## FSTL4       0.0   34.5     32   33.5     32   32.5      0      0   33.5
## SIX6       34.5    0.0     77    0.0      0    0.0     32      0   69.0
## DKK3       34.5   34.5     32   33.5      0   32.5     32     34   33.5
##          iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## OPRD1        90     89   96.0   90.0   91.0   91.0   96.0   96.0    89.0
## SLC2A2       92     88   90.0   89.0   86.0   87.0   76.0   91.0    92.0
## CHL1         91     91   91.0   86.0   89.0   89.0   91.0   88.0    90.0
## GRAMD2B      88     84   95.0   82.0   90.0   90.0   94.0   92.0    85.0
## FOXE1        93     90   87.0   88.0   92.0   92.0   92.0   94.0    91.0
## ELFN1        86     85   92.0   87.0   85.0   88.0   93.0   84.0    81.0
## GABRA2       89     87   94.0   85.0   87.0   85.0   85.0   90.0    88.0
## ARG2         75     81   89.0   79.0   88.0   84.0   95.0   95.0    71.0
## TFCP2L1      84     78   93.0   78.0   69.0   86.0   77.0   93.0    75.0
## BARX1        85     86    0.0   83.0   31.5   31.5   68.0   73.0    86.0
## CLTRN        79     76    0.0   84.0   70.0   67.0   69.0   70.0    82.0
## PCOLCE2      76     80   69.0   81.0   31.5   83.0   33.5   69.0    31.5
## RASGRP1       0     82   75.0   62.0   67.0   66.0    0.0   82.0    72.0
## PLA1A        69     73    0.0   69.0   83.0   77.0   33.5   80.0    73.0
## COMP         82     31   84.0   61.0   72.0   74.0   78.0   33.5    31.5
## MPP1         71     68    0.0   70.0   68.0   82.0    0.0   83.0    78.0
## GLRA1        83     77    0.0   76.0   82.0    0.0    0.0   85.0    87.0
## GCNT4        87     69   79.0   66.0   77.0   65.0   70.0   33.5    74.0
## HCN4         32     66   86.0   75.0   76.0    0.0   90.0   87.0    83.0
## PRELP         0     72   85.0   30.5   31.5   31.5    0.0   33.5    31.5
## RHOT1        72      0   77.0    0.0   75.0   75.0   88.0   33.5    31.5
## MRO          80      0    0.0   73.0   66.0   78.0   82.0   86.0    64.0
## GAD1          0     75   33.5   77.0   31.5   31.5   33.5   33.5    66.0
## NTN1         32     31    0.0   30.5    0.0   73.0   79.0   89.0    31.5
## DACH2        77     74    0.0    0.0   81.0    0.0    0.0    0.0    84.0
## DCX          81     70    0.0   30.5   84.0   81.0   83.0   76.0    31.5
## ARL4C        32     71   33.5   64.0    0.0   31.5    0.0   33.5     0.0
## TBC1D4       74      0    0.0    0.0   64.0   76.0    0.0   77.0    63.0
## CPXM2        32     31   80.0   30.5   31.5   70.0    0.0    0.0    31.5
## FFAR4         0     67    0.0    0.0    0.0    0.0   33.5   81.0    65.0
## SLC24A2      32     31   33.5   30.5   31.5   31.5   33.5   33.5     0.0
## NOTUM        32     31    0.0   80.0   31.5    0.0    0.0   75.0    31.5
## LRRC2        32     31   33.5   30.5   31.5   69.0   75.0   33.5    31.5
## F11          32     83    0.0   67.0   31.5   79.0    0.0    0.0    31.5
## CMTR2        32      0   88.0    0.0   31.5    0.0   71.0   67.0     0.0
## LSAMP        32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## CACNG5       32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## NIPAL4       32     31   33.5   30.5    0.0   31.5    0.0   33.5    31.5
## REEP1        32     31   33.5    0.0   31.5    0.0   33.5   33.5    31.5
## TAGLN3        0     65    0.0    0.0   31.5    0.0    0.0   72.0     0.0
## SERPINE2     32     63    0.0   30.5    0.0   71.0    0.0    0.0     0.0
## CLCF1        32     31   33.5    0.0   31.5    0.0   33.5   33.5     0.0
## C1QTNF1       0      0   68.0    0.0    0.0    0.0    0.0   33.5     0.0
## TSKU         32      0    0.0    0.0    0.0    0.0    0.0    0.0     0.0
## KCNA1        32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## SV2B         65     31    0.0   30.5   31.5   31.5   33.5   33.5    31.5
## CA5B         73     31   73.0   30.5   73.0   68.0   74.0    0.0     0.0
## FSTL4        32     31   33.5   30.5   31.5    0.0   33.5   33.5    31.5
## SIX6          0     64    0.0   72.0    0.0   31.5   33.5    0.0    31.5
## DKK3         32     31    0.0   30.5   31.5    0.0    0.0    0.0    31.5
##          total_rank
## OPRD1        9339.0
## SLC2A2       9078.0
## CHL1         9010.0
## GRAMD2B      8965.0
## FOXE1        8961.0
## ELFN1        8834.0
## GABRA2       8815.0
## ARG2         8426.0
## TFCP2L1      7681.0
## BARX1        7578.0
## CLTRN        7467.0
## PCOLCE2      7353.5
## RASGRP1      7053.0
## PLA1A        6902.5
## COMP         6566.0
## MPP1         6549.5
## GLRA1        6162.5
## GCNT4        5905.0
## HCN4         5878.0
## PRELP        4914.5
## RHOT1        4506.0
## MRO          4381.5
## GAD1         4366.5
## NTN1         4223.0
## DACH2        4060.5
## DCX          4030.5
## ARL4C        3900.0
## TBC1D4       3716.0
## CPXM2        3596.5
## FFAR4        3555.5
## SLC24A2      3395.5
## NOTUM        3341.5
## LRRC2        3318.0
## F11          3260.0
## CMTR2        3231.0
## LSAMP        3193.5
## CACNG5       2974.5
## NIPAL4       2966.0
## REEP1        2966.0
## TAGLN3       2918.5
## SERPINE2     2881.5
## CLCF1        2803.5
## C1QTNF1      2791.0
## TSKU         2782.5
## KCNA1        2765.0
## SV2B         2676.0
## CA5B         2670.5
## FSTL4        2638.5
## SIX6         2615.0
## DKK3         2609.0
write.table(expr_features_comp1_final,file="Comp1_EXPR_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")

expr_features_comp2_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),expr_features_comp2)
rownames(expr_features_comp2_final)<-expr_features_comp2_final$GENE
expr_features_comp2_final$GENE<-NULL
expr_features_comp2_final[is.na(expr_features_comp2_final)]<-0
expr_features_comp2_final$total_rank<-rowSums(expr_features_comp2_final)
expr_features_comp2_final<-expr_features_comp2_final[order(-expr_features_comp2_final$total_rank),]
print(head(expr_features_comp2_final,50))
##          iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## LSAMP       82  90.0  88.0    84  91.0    66  83.0    86  77.0   78.0
## REEP1       86  93.0  93.0    83  96.0    90  87.0    90  83.0   83.0
## CACNG5      92  92.0   0.0    93   0.0    74  95.0    91  86.0   91.0
## FSTL4       83  87.0  92.0    81   0.0     0  89.0    89  75.0   79.0
## SLC24A2     80  80.0  70.0    33  69.0    68  85.0    73  31.5   74.0
## CNTN5        0  89.0   0.0    73  76.0    32  80.0    79  72.0   73.0
## KCNA1       73  88.0  91.0    33   0.0     0  78.0    81  65.0   67.0
## PCOLCE2     75  32.5  85.0    70  79.0    32  77.0    32  31.5   33.5
## NOTUM       69  91.0   0.0    68  86.0     0   0.0    32  79.0    0.0
## GNAL        91   0.0  94.0    88  95.0    86  86.0     0   0.0   88.0
## NEFL         0  66.0  86.0    86  83.0    32   0.0    70  64.0    0.0
## SULF1        0   0.0  89.0    77  94.0    92   0.0     0  78.0    0.0
## TIAM1        0   0.0   0.0    92   0.0     0  96.0    93  90.0   93.0
## NXPH3       93   0.0   0.0    90   0.0     0  93.0     0   0.0   94.0
## GAD1        71  32.5  79.0    71  33.5    32  72.0     0  31.5   33.5
## TSHR        84   0.0   0.0    85   0.0     0  88.0    84  68.0   85.0
## DKK3         0  32.5  82.0    33  82.0    80  33.5    32  31.5   33.5
## ELFN1       32  32.5  69.0    33  68.0    32  71.0    32  31.5   33.5
## SHISAL1      0   0.0   0.0    95   0.0     0   0.0    92  89.0   92.0
## ARL4C       74  72.0   0.0    33  75.0    32  68.0    66  31.5   33.5
## PTCHD4       0   0.0   0.0    87  93.0     0   0.0     0  84.0    0.0
## SFTPA1       0   0.0   0.0    82   0.0     0  90.0     0  81.0   80.0
## BARX1       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## ARG2        32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## CHL1        32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## GABRA2      32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## GRAMD2B     32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## OPRD1       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## SLC2A2      32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## CPXM2        0  32.5  32.5    33  33.5    32   0.0    32  31.5    0.0
## COMP        32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## TFCP2L1     32  32.5  32.5    33  33.5     0  33.5    32  31.5   33.5
## FOXE1       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## PLA1A       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## CLTRN        0  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## NIPAL4      77  69.0   0.0     0   0.0    32  79.0    64  31.5    0.0
## SYT1         0   0.0   0.0     0   0.0     0  91.0    83  76.0   90.0
## RASGRP1     32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## KIAA0319     0   0.0   0.0     0   0.0     0   0.0     0  92.0   96.0
## FSTL5        0   0.0   0.0    72  81.0     0   0.0     0   0.0    0.0
## PRELP        0  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## LRRC2       32   0.0   0.0    33   0.0    79   0.0    32  31.5   33.5
## SV2B        32  32.5  32.5    33  33.5     0  33.5    67   0.0   33.5
## F11          0  32.5   0.0     0   0.0    65   0.0    32  31.5   33.5
## GLRA1       32  77.0  65.0    33  33.5    32   0.0    32  31.5    0.0
## CTSZ         0   0.0  74.0     0  87.0    77   0.0     0   0.0    0.0
## GCNT4       32  32.5  32.5    33  33.5    32  33.5    32  31.5   33.5
## MPP1        32  32.5  32.5    33  33.5    32  33.5     0  31.5   33.5
## HCN4        32  32.5  32.5    33  33.5    32  33.5    32   0.0   33.5
## LRRTM2       0  94.0   0.0    75   0.0    84   0.0    78   0.0    0.0
##          iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19
## LSAMP      83.0     84     90     86     84     82   88.0     87     85
## REEP1      90.0     89      0     90      0     84   94.0     88     93
## CACNG5     87.0     91     93     88     89     88   92.0     93      0
## FSTL4      81.0      0     88     74      0     80   79.0     73     94
## SLC24A2    68.0     80     33     67     31     71   73.0     78     84
## CNTN5      80.0      0      0     80      0     78   80.0     69     88
## KCNA1      78.0      0      0     66     85     66   76.0     34     72
## PCOLCE2    32.5     69     72     73     71     70   75.0     34     34
## NOTUM      82.0      0      0     64     76      0   74.0     81     82
## GNAL        0.0     95      0     89      0      0    0.0     92     83
## NEFL       77.0      0     86     81      0      0   89.0      0     86
## SULF1      86.0      0      0     83      0      0   91.0     94     91
## TIAM1      93.0      0      0      0     91     91   96.0     97      0
## NXPH3       0.0     94      0      0      0     90   95.0      0      0
## GAD1       71.0     33     68     32     72     69    0.0     34     34
## TSHR        0.0     82      0      0     63     72    0.0     77      0
## DKK3       32.5      0     75     68      0      0   34.5     71     34
## ELFN1      32.5     33     33     32     74     32   34.5     34     34
## SHISAL1     0.0      0      0      0     90     92    0.0     95      0
## ARL4C      32.5     33     69     32     65     79   34.5     34     34
## PTCHD4     89.0      0      0      0      0      0   90.0     91     87
## SFTPA1     91.0      0      0      0      0      0   93.0      0     95
## BARX1      32.5     33     33     32     31     32   34.5     34     34
## ARG2       32.5     33     33     32     31     32   34.5     34     34
## CHL1       32.5     33     33     32     31     32   34.5     34     34
## GABRA2     32.5     33     33     32     31     32   34.5     34     34
## GRAMD2B    32.5     33     33     32     31     32   34.5     34     34
## OPRD1      32.5     33     33     32     31     32   34.5     34     34
## SLC2A2     32.5     33     33     32     31     32   34.5     34     34
## CPXM2      32.5     33     33     32     31     32   34.5     34     34
## COMP       32.5     33     33     32     31     32   34.5     34     34
## TFCP2L1    32.5     33     33     32     31     32   34.5     34     34
## FOXE1      32.5     33     33     32     31     32   34.5     34     34
## PLA1A      32.5     33     33     32     31     32   34.5     34     34
## CLTRN      32.5     33     33     32     31      0   34.5     34     34
## NIPAL4     32.5     71     33     32     31     77   34.5     34     34
## SYT1        0.0     92      0      0     88      0    0.0     82      0
## RASGRP1    32.5     33     33     32     31     32   34.5     34     34
## KIAA0319   94.0      0      0      0      0     93   98.0     96      0
## FSTL5      79.0      0      0     82      0      0   85.0      0      0
## PRELP      32.5      0     33     32      0     32   34.5     34     34
## LRRC2      32.5     33      0     32     31     32   34.5     34     34
## SV2B       32.5     67      0     32     31      0   34.5     34     34
## F11        67.0     68     33     32     31     32   34.5      0     34
## GLRA1      32.5      0      0     32     31     32   34.5     34      0
## CTSZ       76.0      0      0     79      0      0   81.0      0     89
## GCNT4      32.5     33      0     32     31     32   34.5     34     34
## MPP1        0.0      0     33     32     31     32   34.5     34     34
## HCN4        0.0     33     33     32     31     32    0.0     34      0
## LRRTM2     84.0      0      0     85      0     75    0.0      0      0
##          iter20 iter21 iter22 iter23 iter24 iter25 iter26 iter27 iter28
## LSAMP      84.0   82.0   83.0     85   93.0   78.0     80     90    0.0
## REEP1      82.0   86.0   86.0      0   92.0   82.0     94     92   92.0
## CACNG5     90.0   81.0   84.0     94   91.0   81.0      0     91    0.0
## FSTL4      80.0   79.0   85.0      0   94.0   76.0      0      0   76.0
## SLC24A2    67.0   84.0   78.0     74   69.0   65.0     73     80   31.5
## CNTN5      78.0   77.0   79.0     84   83.0   70.0      0      0    0.0
## KCNA1      30.5   66.0   32.5     86   75.0   75.0      0      0   85.0
## PCOLCE2    70.0   32.5   32.5     76   33.5   31.5     33     71   31.5
## NOTUM      75.0   78.0   66.0     89   33.5   66.0      0     85   79.0
## GNAL       86.0   73.0   91.0      0    0.0   84.0      0     93    0.0
## NEFL       68.0   70.0   80.0     71   84.0   67.0      0     86   80.0
## SULF1      77.0   85.0   74.0     90   74.0   80.0     91     88   88.0
## TIAM1       0.0    0.0   88.0     97    0.0   88.0      0      0    0.0
## NXPH3      87.0   87.0   92.0      0   95.0   90.0      0      0    0.0
## GAD1       30.5   65.0   32.5     34   71.0   31.5     33     79   78.0
## TSHR        0.0    0.0   75.0     87    0.0   74.0      0      0    0.0
## DKK3       66.0   72.0   32.5     34   72.0   31.5     85     75   77.0
## ELFN1      30.5   32.5   32.5     34   33.5   31.5     33     73   31.5
## SHISAL1     0.0    0.0   93.0      0    0.0   87.0      0      0    0.0
## ARL4C      30.5   32.5   32.5     34   33.5   31.5     33     76   31.5
## PTCHD4     89.0   89.0    0.0      0   82.0    0.0      0      0    0.0
## SFTPA1     85.0   91.0   89.0      0   90.0   86.0      0      0   87.0
## BARX1      30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## ARG2       30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## CHL1       30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## GABRA2     30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## GRAMD2B    30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## OPRD1      30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## SLC2A2     30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## CPXM2      30.5   32.5   32.5     34   33.5   31.5     76     81   68.0
## COMP       30.5   32.5   32.5     34   33.5   31.5     33      0   31.5
## TFCP2L1    30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## FOXE1      30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## PLA1A      30.5   32.5   32.5     34   33.5   31.5     33      0   31.5
## CLTRN      30.5   32.5   32.5     34   33.5   31.5      0     32   31.5
## NIPAL4      0.0   32.5   32.5     72   33.5   31.5     33     83   31.5
## SYT1        0.0    0.0   73.0      0    0.0   77.0      0      0    0.0
## RASGRP1    30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## KIAA0319    0.0    0.0   94.0      0    0.0   91.0      0      0    0.0
## FSTL5       0.0   80.0    0.0     75   78.0   73.0      0     87    0.0
## PRELP      30.5   32.5    0.0     34   33.5   31.5     33     32   31.5
## LRRC2       0.0   32.5   32.5     34   33.5   31.5      0     32   31.5
## SV2B       30.5   32.5   32.5     34    0.0   31.5      0     32    0.0
## F11         0.0    0.0   65.0      0   33.5   31.5     33     78   66.0
## GLRA1      30.5   32.5   32.5     34   33.5   31.5     33     32   69.0
## CTSZ        0.0   71.0    0.0      0   79.0    0.0     74      0   64.0
## GCNT4      30.5   32.5   32.5     34    0.0   31.5     33     32   31.5
## MPP1       30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## HCN4       30.5   32.5   32.5     34   33.5   31.5     33     32   31.5
## LRRTM2      0.0   88.0    0.0     83    0.0   79.0      0      0    0.0
##          iter29 iter30 iter31 iter32 iter33 iter34 iter35 iter36 iter37
## LSAMP        77     93     82   80.0   87.0     83   91.0   86.0     84
## REEP1        87     91     91   87.0   90.0     87   80.0   88.0     93
## CACNG5       88     95     92   89.0   89.0     88   94.0   90.0     82
## FSTL4        78     87     90   81.0   85.0     85   72.0   85.0     81
## SLC24A2      65     76     80   76.0   79.0     71   34.5   70.0     80
## CNTN5        74     81     84   68.0   80.0     81   76.0   84.0      0
## KCNA1        70      0     81   71.0   71.0     80   86.0   33.5     67
## PCOLCE2      66     69     33   65.0   78.0     64   34.5   33.5     32
## NOTUM        81     33     74    0.0   74.0     74    0.0   81.0     77
## GNAL         83      0      0   91.0   91.0     89    0.0    0.0     92
## NEFL         69     82     33    0.0   72.0     77    0.0   72.0     70
## SULF1         0     75     77    0.0    0.0     79    0.0   80.0     91
## TIAM1         0      0      0   92.0   92.0     91   92.0    0.0      0
## NXPH3        91     92     93    0.0    0.0     90   96.0    0.0      0
## GAD1         72     33     72   75.0    0.0     69   34.5   33.5     32
## TSHR         79     88      0   79.0   83.0     72   77.0   71.0      0
## DKK3          0     33     33    0.0   31.5     32   34.5   33.5     71
## ELFN1        32     33     33   72.0   63.0     32   34.5   33.5     32
## SHISAL1      92      0     95   93.0    0.0     93   97.0   94.0      0
## ARL4C        32     33     33    0.0   77.0     32   34.5    0.0     32
## PTCHD4        0     84     83    0.0    0.0      0   74.0   91.0     89
## SFTPA1        0      0      0    0.0    0.0     84    0.0   92.0     90
## BARX1        32     33     33   66.0   31.5     32   34.5   33.5     32
## ARG2         32     33     33   32.5   31.5     32   34.5   33.5     32
## CHL1         32     33     33   32.5   31.5     32   34.5   33.5     32
## GABRA2       32     33     33   32.5   31.5     32   34.5   33.5     32
## GRAMD2B      32     33     33   32.5   31.5     32   34.5   33.5     32
## OPRD1        32     33     33   32.5   31.5     32   34.5   33.5     32
## SLC2A2       32     33     33   32.5   31.5     32   34.5   33.5     32
## CPXM2        32     33     33   32.5   31.5     32    0.0    0.0     66
## COMP         32     33     33   32.5   31.5     32   34.5   33.5     32
## TFCP2L1      32     33      0   32.5   31.5     32   34.5   33.5     32
## FOXE1        32     33     33   32.5   31.5     32   34.5   33.5     32
## PLA1A        32     33     33   32.5   31.5     32   34.5   33.5     32
## CLTRN        32     33     33   32.5   31.5     32   34.5   33.5     32
## NIPAL4        0      0     33    0.0    0.0     32    0.0    0.0     32
## SYT1          0      0      0   90.0   88.0      0   34.5    0.0      0
## RASGRP1      32     33      0   32.5   31.5     32    0.0   33.5     32
## KIAA0319     93      0      0   94.0    0.0      0   98.0   96.0      0
## FSTL5        85     79      0    0.0    0.0      0   84.0   79.0     85
## PRELP        32     33     33   32.5   31.5     32   34.5   33.5     32
## LRRC2        32     33      0   32.5   31.5     32   34.5   33.5      0
## SV2B         32     33     33   32.5   31.5     32    0.0   33.5     32
## F11          32     33     33   69.0   31.5      0    0.0    0.0     32
## GLRA1        32     33      0   32.5   31.5      0   34.5   33.5      0
## CTSZ          0     33      0    0.0    0.0     66    0.0   69.0     88
## GCNT4        32     33     33   32.5   31.5     32    0.0   33.5      0
## MPP1         32     33      0   32.5    0.0     32   34.5   33.5     32
## HCN4          0      0     33   32.5   31.5     32   34.5   33.5     32
## LRRTM2       86     73     87    0.0   76.0      0   82.0    0.0      0
##          iter38 iter39 iter40 iter41 iter42 iter43 iter44 iter45 iter46
## LSAMP        80   80.0     81   72.0     77   87.0   83.0     90     69
## REEP1        71   88.0     85   91.0     86   89.0    0.0     91     86
## CACNG5       90   90.0     82   85.0     83   82.0   92.0     92     83
## FSTL4        34   78.0     79   78.0     74   79.0   84.0     87     70
## SLC24A2      34   32.5     30   32.5     72   85.0   78.0     85     34
## CNTN5        34   75.0     74   79.0      0   77.0   81.0     75     74
## KCNA1         0   83.0     72   32.5      0   81.0   32.5     71     71
## PCOLCE2      34   67.0     75   32.5     32   32.5   32.5     72     34
## NOTUM         0   69.0      0   67.0     65   76.0   79.0     74      0
## GNAL         96   86.0     89    0.0     90   93.0    0.0      0     89
## NEFL          0   73.0      0   81.0     78   78.0    0.0     84     72
## SULF1        75    0.0      0    0.0      0   86.0    0.0     78      0
## TIAM1        93   93.0      0   89.0     91    0.0   94.0      0     90
## NXPH3        89    0.0     88   90.0     89   94.0    0.0      0     95
## GAD1         81   32.5     65   32.5     32   66.0   72.0     33     34
## TSHR         87   74.0     71   65.0     82   73.0   87.0      0     88
## DKK3         34   32.5     61   32.5      0   83.0    0.0     33      0
## ELFN1        34   32.5     30   32.5     32   71.0   32.5     33     34
## SHISAL1      95    0.0      0   93.0     88   90.0   93.0      0     92
## ARL4C        34   32.5     78   32.5     32   68.0   74.0     73      0
## PTCHD4        0    0.0      0   84.0      0    0.0    0.0      0      0
## SFTPA1        0   87.0      0    0.0      0   88.0    0.0      0      0
## BARX1        34   32.5     30   32.5     32   32.5    0.0     33     34
## ARG2         34   32.5     30   32.5     32   32.5   32.5     33     34
## CHL1         34   32.5     30   32.5     32   32.5   32.5     33     34
## GABRA2       34   32.5     30   32.5     32   32.5   32.5     33     34
## GRAMD2B      34   32.5     30   32.5     32   32.5   32.5     33     34
## OPRD1        34   32.5     30   32.5     32   32.5   32.5     33     34
## SLC2A2       34   32.5     30   32.5     32   32.5   32.5     33     34
## CPXM2         0   32.5     30   32.5     32   70.0   32.5     33     34
## COMP         34   32.5     30   32.5     32   65.0   32.5     33      0
## TFCP2L1      34   32.5     30   32.5     32   32.5   32.5     33     34
## FOXE1        34   32.5     30   32.5     32   32.5    0.0     33     34
## PLA1A        34   32.5     30   32.5     32   32.5    0.0     33     34
## CLTRN        34   32.5     30   32.5     32   32.5    0.0     33     34
## NIPAL4        0   32.5     62    0.0     32    0.0   67.0     33      0
## SYT1         84    0.0      0    0.0     84   84.0   89.0      0     78
## RASGRP1      34   32.5     30   32.5      0   32.5   32.5     33     34
## KIAA0319     94    0.0      0    0.0     93    0.0    0.0      0     97
## FSTL5        73    0.0      0   83.0      0    0.0    0.0      0     82
## PRELP        34   32.5      0   32.5     32   32.5   32.5     33     34
## LRRC2         0   32.5     30    0.0     32   32.5   32.5     33     34
## SV2B         34   32.5     30    0.0     32   74.0    0.0     33      0
## F11           0   66.0     30   32.5     32   32.5    0.0     33      0
## GLRA1        34   32.5     63   32.5      0   32.5   32.5     33      0
## CTSZ          0   81.0      0    0.0      0    0.0    0.0     83      0
## GCNT4         0   32.5     30   32.5     32   32.5   32.5     33     34
## MPP1         34   32.5     30   32.5      0   32.5    0.0     33     34
## HCN4         34   32.5      0   32.5      0   32.5   32.5     33     34
## LRRTM2       74   85.0      0   86.0      0    0.0   77.0     88     75
##          iter47 iter48 iter49 iter50 iter51 iter52 iter53 iter54 iter55
## LSAMP      86.0   80.0   90.0     82   86.0     66     89     81     79
## REEP1      87.0   91.0   95.0     90   91.0     89     90     92     82
## CACNG5     92.0   90.0    0.0      0   88.0     88     94     95     77
## FSTL4      81.0    0.0   91.0     81   87.0      0      0     88     70
## SLC24A2    78.0   77.0   84.0     74   85.0     77     76     82     31
## CNTN5      77.0    0.0   88.0      0    0.0      0     85     80     75
## KCNA1      32.5   81.0   87.0      0   82.0      0     34     89     63
## PCOLCE2    74.0   67.0   81.0     84   72.0     69     75     35     31
## NOTUM      67.0   79.0   69.0     80   79.0     75     83     75     68
## GNAL       89.0   94.0    0.0      0   92.0     91     91     96      0
## NEFL       85.0    0.0   83.0     33   74.0      0      0      0     31
## SULF1      83.0   87.0   82.0     83   89.0      0      0      0     78
## TIAM1      93.0   93.0    0.0      0    0.0      0     97      0     87
## NXPH3      88.0   92.0    0.0      0    0.0      0      0     98     85
## GAD1       72.0   66.0    0.0     33   76.0     65     34     74     31
## TSHR       79.0   82.0    0.0      0    0.0      0     82      0      0
## DKK3       32.5   32.5   34.5     76   75.0     74     72      0     31
## ELFN1      32.5   75.0   34.5     33   31.5     68     34     35     31
## SHISAL1     0.0    0.0    0.0      0    0.0      0      0     90     86
## ARL4C      32.5   32.5   70.0     33    0.0      0     34      0     31
## PTCHD4      0.0   88.0   92.0     85    0.0      0     92      0     84
## SFTPA1      0.0    0.0   94.0      0   90.0      0     96      0     88
## BARX1      32.5   32.5   34.5     33   31.5     31     34     35     31
## ARG2       32.5   32.5   34.5     33   31.5     31     34     35     31
## CHL1       32.5   32.5   34.5     33   31.5     31     34     35     31
## GABRA2     32.5   32.5   34.5     33   31.5     31     34     35     31
## GRAMD2B    32.5   32.5   34.5     33   31.5     31     34     35     31
## OPRD1      32.5   32.5   34.5     33   31.5     31     34     35     31
## SLC2A2     32.5   32.5   34.5     33   31.5     31     34     35     31
## CPXM2      32.5   32.5   34.5     33   71.0     72     34     35     31
## COMP       32.5   32.5   34.5     33   65.0     31     34     35     31
## TFCP2L1    32.5   32.5   34.5     33   66.0     31     34     35     31
## FOXE1      32.5   32.5   34.5     33   31.5     31     34     35     31
## PLA1A      32.5    0.0   34.5     33   31.5      0     34     35     31
## CLTRN      32.5   32.5   34.5     33   31.5     31     34     35     31
## NIPAL4     32.5   78.0   71.0     33   31.5     31     34      0      0
## SYT1       82.0    0.0    0.0      0    0.0      0      0      0      0
## RASGRP1    32.5   32.5   34.5     33   31.5     31     34     35     31
## KIAA0319   94.0    0.0    0.0      0    0.0      0      0     99      0
## FSTL5       0.0    0.0   72.0      0    0.0      0     87      0     69
## PRELP       0.0   32.5   34.5     33   31.5     31     34     35     31
## LRRC2      32.5   32.5   34.5      0    0.0     31     34     35     31
## SV2B       32.5   32.5   34.5     33   70.0     31     34     35     31
## F11        32.5   68.0   34.5      0   77.0      0     34      0      0
## GLRA1      32.5   32.5   34.5     33   31.5     63     34      0     31
## CTSZ        0.0    0.0   75.0     94    0.0      0     81      0      0
## GCNT4      32.5   32.5   34.5      0   31.5      0      0     35     31
## MPP1       32.5   32.5   34.5     33   31.5     31     34     35     31
## HCN4       32.5   32.5   34.5      0   31.5     31     34     35     31
## LRRTM2      0.0    0.0    0.0      0    0.0      0      0     78     72
##          iter56 iter57 iter58 iter59 iter60 iter61 iter62 iter63 iter64
## LSAMP        77     82   84.0   75.0   80.0     87   83.0   81.0   76.0
## REEP1        87     86   90.0   81.0   92.0     94   92.0   89.0   92.0
## CACNG5       85     89    0.0   89.0   87.0     93   85.0   87.0   81.0
## FSTL4         0     78    0.0   71.0    0.0     79   84.0    0.0   82.0
## SLC24A2      71     62    0.0   66.0   79.0     83   72.0   71.0   70.0
## CNTN5        78     71   72.0   32.5   73.0     84   78.0   69.0    0.0
## KCNA1        31     72   88.0   32.5   82.0     73   74.0    0.0   74.0
## PCOLCE2      70     67   65.0   32.5   31.5     74   75.0   33.5   79.0
## NOTUM        86     77   82.0   79.0    0.0     89   32.5   33.5   67.0
## GNAL         80     88    0.0   94.0   90.0     86   88.0   92.0    0.0
## NEFL         72     81    0.0    0.0   83.0     78    0.0   73.0   75.0
## SULF1        81     84   89.0    0.0   86.0     90    0.0   75.0    0.0
## TIAM1        91     91    0.0   92.0    0.0      0   89.0   93.0    0.0
## NXPH3         0     90    0.0   91.0    0.0      0   91.0   95.0    0.0
## GAD1         62     31   66.0   32.5   72.0     68   66.0   33.5   32.5
## TSHR         76     85    0.0   88.0    0.0      0   73.0    0.0    0.0
## DKK3         68     31   75.0    0.0   77.0     72   71.0   33.5   32.5
## ELFN1        31     31   32.5   65.0   31.5     33   32.5   33.5   32.5
## SHISAL1       0      0    0.0   90.0    0.0      0    0.0   96.0    0.0
## ARL4C        31     64   77.0    0.0   31.5     33   32.5   68.0   32.5
## PTCHD4       89      0   87.0    0.0    0.0     92   90.0   74.0    0.0
## SFTPA1       88      0   92.0    0.0    0.0     95    0.0   86.0   89.0
## BARX1        31     31   67.0   32.5   31.5     33   32.5   33.5   32.5
## ARG2         31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## CHL1         31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## GABRA2       31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## GRAMD2B      31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## OPRD1        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## SLC2A2       31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## CPXM2        63     31   32.5   32.5   31.5     33    0.0   33.5   32.5
## COMP         31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## TFCP2L1      31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## FOXE1        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## PLA1A        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## CLTRN        31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## NIPAL4       31     76    0.0   69.0   31.5      0   32.5   33.5   32.5
## SYT1          0      0    0.0   85.0    0.0      0   80.0   80.0    0.0
## RASGRP1      31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## KIAA0319      0      0    0.0    0.0    0.0      0   94.0    0.0    0.0
## FSTL5         0      0    0.0    0.0   84.0      0    0.0   72.0   86.0
## PRELP        31     31   32.5   32.5   31.5     33   32.5   33.5    0.0
## LRRC2        31     31   32.5    0.0   31.5     33   32.5   33.5   32.5
## SV2B         31     31   32.5   32.5   31.5     33   32.5    0.0    0.0
## F11          31     65    0.0    0.0   31.5      0   32.5   33.5    0.0
## GLRA1        65     31   32.5    0.0   31.5     70   32.5   33.5   32.5
## CTSZ          0      0   81.0    0.0   31.5     76    0.0   33.5   87.0
## GCNT4         0     31   32.5   32.5   31.5     33   32.5    0.0    0.0
## MPP1         31     31   32.5   32.5   31.5     33   32.5   33.5   32.5
## HCN4         31      0   32.5   32.5    0.0     33   32.5   33.5   32.5
## LRRTM2        0      0    0.0    0.0   81.0      0    0.0    0.0   90.0
##          iter65 iter66 iter67 iter68 iter69 iter70 iter71 iter72 iter73
## LSAMP      73.0     87     80     78   80.0     77     74   86.0     76
## REEP1      86.0     91      0     80   86.0     81     83   95.0     90
## CACNG5     85.0     90     87     84   87.0     87     82   89.0     86
## FSTL4      76.0     89     74     82   79.0     79     80   85.0     71
## SLC24A2    32.5     83     34     72   66.0     80     69   80.0     74
## CNTN5      78.0     85     34     75   82.0     76     76   75.0     70
## KCNA1      32.5     64     34     68   74.0     72     68   71.0     72
## PCOLCE2    32.5     79     34     32   32.5     34     63   34.5     80
## NOTUM      82.0     65      0     64   77.0     68     64   34.5     82
## GNAL       83.0     88     83      0   88.0     86     85   91.0      0
## NEFL       74.0      0      0     32   67.0     73     75   83.0     31
## SULF1      84.0      0      0     65    0.0     69      0    0.0     88
## TIAM1      91.0     92     91     92   93.0     95     87   96.0      0
## NXPH3      90.0      0     96     86   92.0     93     81   93.0      0
## GAD1        0.0     69     34     32   70.0     34     29   34.5      0
## TSHR       75.0     73     77     71   83.0     83     65   34.5      0
## DKK3       32.5     68      0     32   32.5     34      0   34.5     31
## ELFN1      32.5     32     34     32   32.5     34     29   34.5     31
## SHISAL1     0.0     93     92     90   89.0     88     84   92.0      0
## ARL4C       0.0      0     34     32    0.0     34     29   34.5     64
## PTCHD4      0.0      0     78      0    0.0     82      0    0.0      0
## SFTPA1      0.0      0      0      0    0.0     84      0    0.0      0
## BARX1      32.5     32     34     32   32.5     34     29   34.5     31
## ARG2       32.5     32     34     32   32.5     34     29   34.5     31
## CHL1       32.5     32     34     32   32.5     34     29   34.5     31
## GABRA2     32.5     32     34     32   32.5     34     29   34.5     31
## GRAMD2B    32.5     32     34     32   32.5     34     29   34.5     31
## OPRD1      32.5     32     34     32   32.5     34     29   34.5     31
## SLC2A2     32.5     32     34     32   32.5     34     29   34.5     31
## CPXM2      32.5      0     34     32   32.5     34     29   34.5     31
## COMP       32.5     32     34     32   32.5     34     29   34.5     31
## TFCP2L1    32.5     32     34     32   32.5     34     29   34.5     31
## FOXE1      32.5     32     34     32   32.5     34     29   34.5     31
## PLA1A      32.5     32     34     32   32.5     34     29   34.5     31
## CLTRN      32.5     32     34     32   32.5     34     29   34.5     31
## NIPAL4     32.5      0     34     32   32.5     34     29   34.5     65
## SYT1       79.0     81     71     67   78.0      0     79   34.5      0
## RASGRP1    32.5     32     34     32   32.5     34     29   34.5     31
## KIAA0319   94.0      0     97     93   94.0     97      0   98.0      0
## FSTL5      81.0      0      0      0    0.0     70      0    0.0     89
## PRELP      32.5      0     34     32   32.5     34      0   34.5     31
## LRRC2      32.5     32     34     32   32.5     34     29   34.5     31
## SV2B       32.5     32     34     32   32.5     34     29   34.5     31
## F11        32.5     75     34     32    0.0     34     61    0.0     63
## GLRA1      32.5     32     34     32   32.5     34      0    0.0     31
## CTSZ        0.0      0      0      0    0.0      0      0    0.0     83
## GCNT4      32.5     32     34     32   32.5      0     29   34.5     31
## MPP1       32.5     32     34     32   32.5     34     29   34.5     31
## HCN4       32.5     32      0     32   32.5     34     29    0.0      0
## LRRTM2     87.0      0      0      0    0.0      0      0    0.0     91
##          iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## LSAMP      84.0   84.0     83     90   90.0   93.0    0.0     86   88.0
## REEP1      88.0   85.0     94     95   92.0   96.0    0.0     93   90.0
## CACNG5     87.0   89.0      0     91   93.0    0.0   93.0     89   93.0
## FSTL4      81.0   78.0     82     92   71.0   92.0    0.0     77   89.0
## SLC24A2    75.0   32.5     72     80   69.0   73.0   71.0     33   32.5
## CNTN5      79.0   83.0     33     83   85.0    0.0    0.0     80   83.0
## KCNA1      76.0   32.5      0     89   77.0    0.0   81.0     73   70.0
## PCOLCE2    30.5   70.0     77     76   72.0   80.0   32.5     74   66.0
## NOTUM       0.0    0.0     85     78   87.0   70.0    0.0     72   80.0
## GNAL       86.0   79.0      0     81   91.0    0.0    0.0     92    0.0
## NEFL       85.0   71.0     33     79   88.0   89.0    0.0     70   82.0
## SULF1       0.0   80.0     91     82   80.0   95.0    0.0     78   86.0
## TIAM1      90.0   93.0      0      0    0.0    0.0    0.0      0    0.0
## NXPH3      89.0    0.0      0      0   94.0    0.0    0.0      0    0.0
## GAD1       62.0   32.5     33     33   66.0    0.0   77.0     33   74.0
## TSHR       69.0    0.0      0      0   83.0    0.0   88.0      0   77.0
## DKK3        0.0   32.5     79     73   32.5   75.0    0.0     82   71.0
## ELFN1      30.5   32.5     33     33   32.5   34.5   69.0     33   65.0
## SHISAL1     0.0    0.0      0      0    0.0    0.0    0.0      0    0.0
## ARL4C      65.0   32.5     33     33    0.0   34.5   67.0     33   32.5
## PTCHD4      0.0   87.0     90      0    0.0   94.0    0.0     91    0.0
## SFTPA1      0.0    0.0      0      0    0.0    0.0    0.0      0   94.0
## BARX1      30.5   32.5     33     33   32.5   34.5    0.0     33   32.5
## ARG2       30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## CHL1       30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## GABRA2     30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## GRAMD2B    30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## OPRD1      30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## SLC2A2     30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## CPXM2      30.5   32.5     70     33   32.5   34.5    0.0     33   32.5
## COMP       30.5   32.5     33     33    0.0   34.5   32.5     33   32.5
## TFCP2L1    30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## FOXE1      30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## PLA1A      30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## CLTRN      30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## NIPAL4     61.0    0.0     33     33    0.0   34.5   72.0      0   32.5
## SYT1       83.0    0.0      0      0    0.0    0.0   91.0      0    0.0
## RASGRP1    30.5   32.5     33     33   32.5   34.5    0.0     33   32.5
## KIAA0319    0.0    0.0      0      0    0.0    0.0    0.0      0    0.0
## FSTL5       0.0   82.0      0      0    0.0   87.0    0.0     85   84.0
## PRELP      30.5   32.5     33     33   32.5   34.5   32.5     33   32.5
## LRRC2      30.5   32.5     67     33   32.5   34.5   32.5     33   32.5
## SV2B       30.5    0.0      0     33   32.5    0.0    0.0     66   32.5
## F11        63.0   32.5      0     33    0.0   77.0   32.5     33    0.0
## GLRA1      30.5   32.5     33     70   32.5   34.5   32.5     33   32.5
## CTSZ        0.0   74.0     86     86    0.0   85.0    0.0     83   85.0
## GCNT4      30.5   32.5     33     33   32.5    0.0   32.5     33   32.5
## MPP1       30.5    0.0     33     33   32.5   34.5   32.5     33   32.5
## HCN4       30.5    0.0     33     33   32.5   34.5   32.5     33   32.5
## LRRTM2      0.0   81.0      0      0    0.0    0.0    0.0     87   87.0
##          iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## LSAMP      82.0   89.0     87   81.0     72   83.0     79     76   80.0
## REEP1      89.0   86.0     92   77.0     81    0.0     92     95   83.0
## CACNG5     95.0   94.0      0   84.0     83   92.0     91     92   92.0
## FSTL4       0.0   82.0     88   76.0     68   79.0      0      0   81.0
## SLC24A2     0.0   74.0     79   80.0     69   80.0     77     77   33.5
## CNTN5      34.5   75.0      0   33.5     70   73.0     78      0   79.0
## KCNA1      81.0   72.0     83   33.5     75   69.0      0     83   33.5
## PCOLCE2    34.5   34.5     76   33.5     32   70.0     65     73   74.0
## NOTUM       0.0   71.0     82   33.5      0   82.0     80      0   33.5
## GNAL        0.0   92.0      0   78.0     85    0.0      0     91    0.0
## NEFL        0.0   79.0     77   69.0     65   84.0     76      0   82.0
## SULF1      74.0   76.0     86    0.0     82   88.0     86      0   76.0
## TIAM1      97.0   95.0      0   94.0     92   94.0      0      0   93.0
## NXPH3      91.0   93.0      0   92.0     90   89.0      0     96   91.0
## GAD1       34.5   34.5      0   33.5     32   32.5     32     71   33.5
## TSHR        0.0   83.0      0   70.0     78   85.0      0      0   73.0
## DKK3       34.5   34.5     73   33.5      0   32.5     64     34   33.5
## ELFN1      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## SHISAL1    98.0   97.0      0   90.0     88    0.0      0      0   94.0
## ARL4C      34.5   34.5     32   33.5      0    0.0     32     34   33.5
## PTCHD4     83.0   88.0     91    0.0      0    0.0     89      0   85.0
## SFTPA1     84.0   96.0     90   88.0      0   90.0     88      0   87.0
## BARX1      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## ARG2       34.5   34.5     32   33.5     32   32.5     32     34   33.5
## CHL1       34.5   34.5     32   33.5     32   32.5     32     34   33.5
## GABRA2     34.5   34.5     32   33.5     32   32.5     32     34   33.5
## GRAMD2B    34.5   34.5     32   33.5     32   32.5     32     34   33.5
## OPRD1      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## SLC2A2     34.5   34.5     32   33.5     32   32.5     32     34   33.5
## CPXM2      34.5   34.5     66    0.0      0   32.5     32     34   33.5
## COMP       34.5   34.5     32   33.5     32   32.5     32     34   33.5
## TFCP2L1    34.5   34.5     32   33.5      0   32.5     32     34   33.5
## FOXE1      34.5   34.5     32   33.5     32   32.5     32      0   33.5
## PLA1A      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## CLTRN      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## NIPAL4     34.5    0.0     32   33.5     32    0.0     32      0   33.5
## SYT1        0.0   90.0      0   73.0     77    0.0      0      0    0.0
## RASGRP1    34.5   34.5     32   33.5      0   32.5     32      0   33.5
## KIAA0319    0.0   98.0      0   96.0     93    0.0      0      0   95.0
## FSTL5      34.5    0.0     89   67.0      0    0.0     83      0    0.0
## PRELP      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## LRRC2      34.5   34.5      0   33.5     32   32.5     32     34   33.5
## SV2B        0.0   34.5     71   33.5     32   32.5     32      0    0.0
## F11        34.5   34.5     67   33.5     32   32.5      0      0   33.5
## GLRA1       0.0    0.0     32   33.5      0   32.5     32      0   33.5
## CTSZ       34.5    0.0     84    0.0      0    0.0     72      0    0.0
## GCNT4      34.5   34.5     32   33.5     32   32.5     32     34   33.5
## MPP1       34.5    0.0     32   33.5      0   32.5     32      0    0.0
## HCN4       34.5   34.5     32   33.5      0   32.5     32     34   33.5
## LRRTM2     70.0    0.0      0    0.0      0    0.0      0     86    0.0
##          iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## LSAMP        89     83   76.0   81.0   82.0   84.0   86.0   84.0    84.0
## REEP1        93     88   81.0    0.0   88.0    0.0   85.0   79.0    90.0
## CACNG5       90     86   90.0   77.0   86.0   85.0   92.0   92.0    92.0
## FSTL4        84     75   68.0   75.0   75.0    0.0   88.0   87.0    86.0
## SLC24A2      80     31   70.0   65.0   70.0   75.0   68.0   75.0     0.0
## CNTN5        88     80   67.0   74.0    0.0    0.0    0.0   78.0    83.0
## KCNA1        85     81   33.5   72.0   76.0   76.0   71.0   33.5    87.0
## PCOLCE2      76     63   33.5   64.0   71.0   69.0   67.0   71.0    72.0
## NOTUM        67     76    0.0   76.0   69.0    0.0    0.0   80.0    75.0
## GNAL          0     89    0.0    0.0   89.0   91.0   87.0    0.0     0.0
## NEFL         86     82    0.0   73.0   31.5    0.0    0.0    0.0    76.0
## SULF1         0     79    0.0   83.0   84.0    0.0    0.0    0.0    78.0
## TIAM1         0      0   95.0    0.0    0.0   92.0   93.0   95.0     0.0
## NXPH3        91     90   91.0    0.0    0.0   86.0   95.0    0.0     0.0
## GAD1          0     71   33.5   30.5   67.0   31.5   80.0   33.5    31.5
## TSHR          0     74   86.0   30.5    0.0   78.0   74.0   82.0     0.0
## DKK3         69     31    0.0   80.0   63.0    0.0    0.0    0.0    31.5
## ELFN1        32     31   33.5   30.5   31.5   64.0   33.5   33.5    31.5
## SHISAL1       0      0   93.0    0.0    0.0   90.0   96.0   91.0     0.0
## ARL4C        32     31   33.5   30.5    0.0   31.5    0.0   33.5     0.0
## PTCHD4        0     85    0.0    0.0    0.0    0.0    0.0   88.0    89.0
## SFTPA1        0     87    0.0    0.0   90.0    0.0    0.0    0.0     0.0
## BARX1        32     31    0.0   30.5   66.0   31.5   33.5   33.5    31.5
## ARG2         32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## CHL1         32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## GABRA2       32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## GRAMD2B      32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## OPRD1        32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## SLC2A2       32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## CPXM2        32     31   33.5   71.0   31.5   31.5    0.0    0.0    31.5
## COMP         32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## TFCP2L1      32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## FOXE1        32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## PLA1A        32     31    0.0   30.5   31.5   31.5   33.5   33.5    31.5
## CLTRN        32     31    0.0   30.5   31.5   31.5   33.5   33.5    31.5
## NIPAL4       32     31   33.5   30.5    0.0   31.5    0.0   70.0    70.0
## SYT1         77      0   89.0    0.0    0.0   87.0   91.0   85.0     0.0
## RASGRP1       0     31   33.5   30.5   31.5   31.5    0.0   33.5    31.5
## KIAA0319      0     91   96.0    0.0    0.0    0.0    0.0   93.0     0.0
## FSTL5         0     78    0.0    0.0    0.0    0.0    0.0    0.0    80.0
## PRELP         0     31   33.5   30.5   31.5   31.5    0.0   33.5    31.5
## LRRC2        32     31   33.5   70.0   31.5   31.5   33.5   33.5    31.5
## SV2B         32     31    0.0   30.5   78.0   31.5   33.5   33.5    65.0
## F11          32     31    0.0   30.5   64.0   31.5    0.0    0.0    68.0
## GLRA1        32     31    0.0   30.5   31.5    0.0    0.0   33.5    31.5
## CTSZ         79      0    0.0   84.0    0.0    0.0    0.0    0.0    77.0
## GCNT4        32     31   33.5   30.5   31.5   31.5   33.5   33.5    31.5
## MPP1         32     31    0.0   30.5   31.5   31.5    0.0   33.5    31.5
## HCN4         32     31   33.5   30.5   31.5    0.0   33.5   33.5    31.5
## LRRTM2        0      0    0.0    0.0    0.0    0.0    0.0    0.0    81.0
##          total_rank
## LSAMP        8102.0
## REEP1        8033.0
## CACNG5       7878.0
## FSTL4        6522.0
## SLC24A2      6454.5
## CNTN5        5769.5
## KCNA1        5699.5
## PCOLCE2      5570.0
## NOTUM        5434.0
## GNAL         5309.0
## NEFL         5133.5
## SULF1        5060.0
## TIAM1        4808.0
## NXPH3        4746.0
## GAD1         4502.0
## TSHR         4457.0
## DKK3         4055.5
## ELFN1        3773.0
## SHISAL1      3767.0
## ARL4C        3517.0
## PTCHD4       3479.0
## SFTPA1       3377.0
## BARX1        3261.5
## ARG2         3257.5
## CHL1         3257.5
## GABRA2       3257.5
## GRAMD2B      3257.5
## OPRD1        3257.5
## SLC2A2       3257.5
## CPXM2        3251.0
## COMP         3225.0
## TFCP2L1      3195.0
## FOXE1        3191.0
## PLA1A        3096.0
## CLTRN        3094.5
## NIPAL4       3077.0
## SYT1         3043.0
## RASGRP1      2994.0
## KIAA0319     2941.0
## FSTL5        2921.5
## PRELP        2907.5
## LRRC2        2889.0
## SV2B         2864.5
## F11          2856.0
## GLRA1        2850.5
## CTSZ         2842.5
## GCNT4        2827.0
## MPP1         2797.5
## HCN4         2707.0
## LRRTM2       2705.0
write.table(expr_features_comp2_final,file="Comp2_EXPR_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")


meth_features_comp1_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),meth_features_comp1)
rownames(meth_features_comp1_final)<-meth_features_comp1_final$GENE
meth_features_comp1_final$GENE<-NULL
meth_features_comp1_final[is.na(meth_features_comp1_final)]<-0
meth_features_comp1_final$total_rank<-rowSums(meth_features_comp1_final)
meth_features_comp1_final<-meth_features_comp1_final[order(-meth_features_comp1_final$total_rank),]
print(head(meth_features_comp1_final,50))
##            iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## cg02988288 100.0 100.0 100.0 100.0  92.0  92.0 100.0 100.0  97.0     99
## cg02966936  93.0  96.0  96.0  94.0  96.0  83.0  97.0  99.0  90.0     93
## cg07175985  89.0  92.0   0.0  93.0 100.0 100.0  93.0  96.0 100.0     92
## cg14527110  98.0  89.0  35.5  97.0  91.0  89.0  98.0  98.0  85.0     35
## cg12220370  96.0  75.0  99.0  91.0  74.0  93.0  94.0  78.0  89.0     98
## cg13566279  99.0  91.0  79.0  96.0  97.0   0.0  99.0  94.0  99.0     87
## cg14490520  76.0  99.0   0.0  86.0  99.0  98.0  86.0  93.0  88.0     74
## cg03770217  82.0  73.0  35.5  98.0  75.0   0.0  75.0  76.0  93.0      0
## cg06184251  35.5  93.0   0.0  35.5  85.0  96.0  83.0  92.0  87.0      0
## cg25934997  85.0  88.0   0.0   0.0   0.0   0.0  96.0  97.0  95.0      0
## cg04577129  71.0   0.0  97.0  83.0  71.0  77.0  87.0  87.0  78.0     95
## cg25979005  73.0  80.0  85.0  77.0  78.0   0.0  71.0  88.0  92.0     84
## cg09467248  35.5  77.0  35.5  99.0  83.0   0.0  85.0  79.0  96.0      0
## cg17826980  72.0  90.0  91.0  35.5  89.0  81.0  35.5  81.0  77.0      0
## cg21165486  95.0   0.0  82.0  90.0   0.0   0.0  77.0  35.5  71.0     96
## cg02736232  35.5  97.0  98.0  35.5   0.0   0.0  84.0  71.0  91.0      0
## cg13336515  86.0  79.0  71.0  35.5  35.5  72.0  35.5  35.5  84.0     82
## cg11515284  97.0  95.0  94.0  35.5  72.0  94.0  35.5  35.5  35.5     35
## cg26767974  92.0   0.0  83.0  72.0   0.0   0.0  81.0  86.0  86.0     77
## cg05627498  94.0  35.5  89.0  89.0  81.0   0.0  90.0  35.5  72.0     72
## cg09449232  84.0  94.0   0.0  92.0  88.0  82.0  79.0  85.0   0.0     35
## cg13176806  79.0   0.0   0.0  88.0   0.0   0.0  88.0  72.0   0.0     83
## cg14534405  75.0  87.0   0.0  84.0  86.0  74.0  35.5   0.0   0.0      0
## cg22364465  91.0   0.0  86.0  35.5  80.0   0.0  95.0  83.0  35.5     88
## cg15275625   0.0  71.0  80.0  95.0  90.0  85.0  35.5   0.0  80.0      0
## cg11743000  90.0   0.0   0.0  85.0  76.0   0.0  89.0   0.0   0.0     94
## cg04255401   0.0  35.5   0.0  87.0  94.0  35.5  92.0  35.5  35.5     75
## cg06749277   0.0   0.0  78.0  79.0  77.0   0.0  78.0  84.0   0.0     73
## cg27044597  35.5   0.0  35.5  35.5  87.0   0.0  35.5  35.5  35.5      0
## cg24196354   0.0   0.0   0.0  76.0   0.0   0.0  91.0  89.0   0.0      0
## cg03220447   0.0  98.0   0.0   0.0  35.5   0.0   0.0  74.0  94.0      0
## cg21533994  35.5  82.0  35.5  35.5  35.5  95.0  35.5  35.5  35.5     35
## cg03726357  35.5  76.0   0.0  35.5  35.5  78.0  35.5  35.5  35.5      0
## cg08248985  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg12451325  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg04934500  80.0   0.0  88.0  80.0   0.0   0.0  73.0   0.0  83.0      0
## cg07270865  74.0  35.5  81.0   0.0   0.0   0.0   0.0   0.0  35.5     85
## cg15630265  35.5   0.0  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg26079959   0.0  81.0   0.0  35.5  35.5  73.0  35.5  35.5  35.5      0
## cg13970113  35.5   0.0  35.5  35.5  35.5   0.0  35.5   0.0  35.5     35
## cg13090941  35.5   0.0  35.5  35.5  35.5  35.5  35.5  90.0  35.5      0
## cg26445440  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg27539060   0.0   0.0  77.0  75.0  95.0   0.0  35.5   0.0  79.0      0
## cg19484548   0.0   0.0  95.0  35.5  98.0  97.0   0.0   0.0   0.0      0
## cg24486540  35.5  35.5  35.5   0.0   0.0  87.0  35.5  35.5   0.0     35
## cg09216797  35.5   0.0  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg00970981  35.5  35.5  35.5   0.0   0.0   0.0  35.5  35.5   0.0     35
## cg24794608   0.0   0.0  92.0   0.0   0.0   0.0  76.0   0.0  81.0      0
## cg12164242  35.5   0.0  35.5  35.5   0.0   0.0  35.5  35.5  35.5     35
## cg03622758  35.5   0.0  35.5  35.5   0.0   0.0  35.5  35.5  35.5     35
##            iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19
## cg02988288   97.0  100.0   97.0   95.0   97.0  100.0     99  100.0   99.0
## cg02966936   90.0   35.5   96.0   90.0   94.0   98.0     94   98.0   97.0
## cg07175985   99.0   96.0   93.0  100.0   81.0   96.0     91   95.0   94.0
## cg14527110   98.0   35.5  100.0   89.0  100.0   97.0     93   82.0  100.0
## cg12220370   93.0   99.0    0.0   87.0   92.0   35.5     98   92.0   91.0
## cg13566279   91.0   93.0    0.0   98.0   95.0   99.0     96   96.0   93.0
## cg14490520  100.0    0.0   99.0   88.0    0.0   95.0     95   97.0   83.0
## cg03770217   84.0   97.0   83.0   99.0   82.0   83.0     79   35.5   35.5
## cg06184251   96.0   35.5   94.0   35.5   35.5   35.5     97   89.0   87.0
## cg25934997   81.0   88.0    0.0   97.0   73.0   88.0     77   90.0   81.0
## cg04577129   83.0   98.0    0.0   85.0    0.0   75.0     89   78.0   35.5
## cg25979005   89.0   35.5   79.0   96.0   71.0   94.0     80   85.0   79.0
## cg09467248   87.0   35.5   92.0   77.0   99.0   85.0     81    0.0   35.5
## cg17826980   92.0    0.0   98.0    0.0   79.0   86.0     84   91.0   98.0
## cg21165486   72.0   85.0    0.0   35.5   84.0   93.0     72   76.0   96.0
## cg02736232   79.0    0.0    0.0   35.5   35.5   35.5     90    0.0   72.0
## cg13336515   35.5    0.0   74.0   74.0   35.5   35.5     35   72.0   76.0
## cg11515284   80.0    0.0   76.0    0.0    0.0   35.5      0   35.5   90.0
## cg26767974   73.0   95.0    0.0   79.0   72.0   84.0     86   88.0   35.5
## cg05627498   35.5   86.0    0.0   35.5   89.0   92.0     71   83.0   35.5
## cg09449232   75.0    0.0   86.0   81.0   75.0   35.5      0   86.0   95.0
## cg13176806    0.0   84.0    0.0   84.0   86.0   89.0     75   93.0    0.0
## cg14534405   82.0   35.5    0.0   35.5   93.0   35.5     82    0.0   35.5
## cg22364465    0.0   92.0    0.0   71.0   88.0   90.0     87   84.0   77.0
## cg15275625   77.0    0.0   87.0    0.0   98.0   35.5     88    0.0   85.0
## cg11743000   86.0    0.0    0.0    0.0   83.0   76.0     78   94.0    0.0
## cg04255401   35.5    0.0    0.0   35.5   35.5   35.5     35   79.0   35.5
## cg06749277    0.0   87.0    0.0   83.0    0.0   79.0      0   87.0    0.0
## cg27044597   35.5    0.0   80.0   35.5   80.0   35.5     35   35.5   75.0
## cg24196354   35.5   83.0    0.0   72.0    0.0    0.0     83    0.0    0.0
## cg03220447   94.0   35.5    0.0    0.0   35.5   35.5     92    0.0   35.5
## cg21533994   88.0   35.5   90.0    0.0    0.0   35.5     35   35.5    0.0
## cg03726357   74.0    0.0   91.0   35.5   85.0    0.0     35   35.5   71.0
## cg08248985   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg12451325   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg04934500    0.0   91.0    0.0   93.0    0.0   82.0      0   74.0    0.0
## cg07270865    0.0   35.5    0.0    0.0    0.0   77.0      0    0.0    0.0
## cg15630265   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5    0.0
## cg26079959   85.0    0.0   88.0    0.0   35.5    0.0     35    0.0    0.0
## cg13970113    0.0   35.5    0.0   35.5   35.5   35.5     35   35.5    0.0
## cg13090941   35.5   35.5   95.0   35.5   35.5   78.0      0   35.5   78.0
## cg26445440   35.5    0.0    0.0   35.5    0.0   35.5     35   35.5   35.5
## cg27539060    0.0   75.0    0.0   92.0   74.0    0.0      0    0.0    0.0
## cg19484548    0.0    0.0    0.0    0.0   78.0    0.0      0   99.0   73.0
## cg24486540   35.5   35.5   35.5    0.0    0.0   35.5     35   35.5   35.5
## cg09216797   35.5   35.5    0.0    0.0   35.5   35.5     35   35.5   35.5
## cg00970981   35.5   35.5    0.0    0.0    0.0   35.5      0   35.5    0.0
## cg24794608    0.0    0.0    0.0   86.0    0.0    0.0      0   77.0    0.0
## cg12164242   35.5    0.0   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg03622758    0.0   35.5    0.0   35.5   35.5   35.5     35   35.5    0.0
##            iter20 iter21 iter22 iter23 iter24 iter25 iter26 iter27 iter28
## cg02988288     99  100.0   99.0   99.0  100.0     95   91.0  100.0   98.0
## cg02966936     93   99.0   85.0   95.0   98.0     94   97.0   90.0   85.0
## cg07175985     90   88.0   94.0   97.0   99.0     98  100.0   87.0   75.0
## cg14527110     97   93.0   97.0   93.0   93.0     97   71.0   99.0   82.0
## cg12220370     98   91.0   86.0   76.0   97.0     88   88.0   94.0   73.0
## cg13566279     86   98.0  100.0  100.0   94.0     83   98.0   95.0   83.0
## cg14490520     94   35.5   89.0   96.0   95.0     96   94.0   86.0   92.0
## cg03770217     87   73.0   87.0   92.0   90.0     70   35.5   98.0    0.0
## cg06184251     82   81.0   83.0   94.0   84.0     93   93.0   97.0   99.0
## cg25934997     35   78.0   95.0   86.0   82.0     80   79.0   91.0   91.0
## cg04577129     35   95.0   93.0   91.0   87.0     75    0.0   92.0    0.0
## cg25979005     35   80.0   96.0   87.0   35.5     92   35.5   84.0    0.0
## cg09467248     91   97.0   98.0   89.0   92.0     74   35.5   71.0   93.0
## cg17826980     79   35.5   35.5   35.5   73.0     99   99.0   72.0  100.0
## cg21165486     81   94.0   35.5   35.5   35.5      0    0.0    0.0    0.0
## cg02736232     80   35.5   80.0   80.0   88.0     85   83.0   96.0    0.0
## cg13336515     89   83.0   73.0   35.5   86.0     89   81.0   77.0    0.0
## cg11515284     76   71.0   75.0   88.0   89.0     90   92.0   35.5    0.0
## cg26767974      0   35.5   91.0   35.5    0.0     35    0.0   35.5   79.0
## cg05627498     77   35.5   76.0   75.0   75.0      0    0.0   79.0   89.0
## cg09449232     74   82.0   81.0   90.0   80.0      0   89.0   35.5   71.0
## cg13176806     88   96.0   90.0   71.0   74.0      0    0.0   93.0    0.0
## cg14534405     85   35.5   35.5   98.0    0.0     72    0.0   74.0   35.5
## cg22364465      0   72.0   92.0   35.5    0.0      0    0.0   80.0   86.0
## cg15275625     92   87.0    0.0    0.0   85.0     79    0.0   35.5   96.0
## cg11743000      0   86.0   77.0   72.0    0.0     86   84.0   88.0   94.0
## cg04255401     73   75.0   35.5   74.0   91.0      0   86.0   35.5   35.5
## cg06749277      0    0.0   35.5    0.0   79.0      0    0.0   78.0    0.0
## cg27044597     35   35.5    0.0    0.0   35.5     84   78.0    0.0   35.5
## cg24196354      0   85.0   78.0    0.0   96.0      0    0.0   35.5    0.0
## cg03220447     95    0.0    0.0   35.5    0.0     91    0.0   35.5   97.0
## cg21533994     35    0.0   35.5   35.5    0.0     87   35.5    0.0   87.0
## cg03726357     35   35.5    0.0   78.0   35.5     35   82.0   35.5    0.0
## cg08248985     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg12451325     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg04934500      0    0.0   88.0   85.0    0.0      0    0.0   89.0    0.0
## cg07270865      0   35.5   84.0    0.0    0.0      0    0.0   76.0   35.5
## cg15630265     35   35.5   35.5   35.5   35.5     35    0.0   35.5   35.5
## cg26079959     35    0.0   35.5   35.5    0.0     82   35.5    0.0   90.0
## cg13970113     35   35.5   35.5   35.5   35.5     35    0.0   35.5   35.5
## cg13090941      0    0.0   35.5    0.0   35.5     35    0.0   35.5   35.5
## cg26445440     35   35.5   35.5    0.0   35.5     35   35.5   35.5   35.5
## cg27539060      0    0.0   82.0    0.0   83.0      0    0.0    0.0    0.0
## cg19484548      0   35.5    0.0    0.0    0.0      0   90.0    0.0   95.0
## cg24486540     35    0.0   35.5    0.0   35.5     35   35.5   35.5    0.0
## cg09216797     35   35.5   35.5   35.5    0.0      0   35.5   35.5    0.0
## cg00970981     35    0.0   35.5   35.5    0.0     35   35.5   35.5   35.5
## cg24794608      0    0.0    0.0    0.0    0.0      0   80.0   35.5    0.0
## cg12164242     35   35.5    0.0   35.5   35.5     35    0.0   35.5   35.5
## cg03622758     35   35.5   35.5   35.5   35.5     35    0.0   35.5    0.0
##            iter29 iter30 iter31 iter32 iter33 iter34 iter35 iter36 iter37
## cg02988288  100.0     99   99.0  100.0  100.0  100.0     98  100.0   99.0
## cg02966936   94.0     97   72.0   95.0   99.0   99.0     97   97.0   89.0
## cg07175985   95.0     75   90.0   91.0   96.0   89.0     96   98.0   75.0
## cg14527110   98.0     96   98.0   98.0   98.0   88.0     93   95.0   74.0
## cg12220370   74.0     95   87.0   92.0   84.0   98.0     99   73.0   96.0
## cg13566279   82.0     92   96.0   97.0   95.0   93.0     94   99.0    0.0
## cg14490520   96.0     98   97.0   87.0   93.0   74.0     95   88.0   95.0
## cg03770217   85.0     88   77.0   85.0   75.0   78.0     90   84.0    0.0
## cg06184251   84.0     82   79.0   35.5   87.0   35.5     35   85.0  100.0
## cg25934997   97.0     73    0.0   74.0   90.0   96.0      0   94.0    0.0
## cg04577129   79.0     93   88.0   86.0   82.0   97.0     79    0.0   82.0
## cg25979005   89.0     74    0.0   96.0   81.0   35.5     82    0.0    0.0
## cg09467248   90.0     85   84.0   76.0   86.0    0.0     88   91.0   80.0
## cg17826980   93.0     35   35.5   99.0   89.0   35.5      0   78.0   98.0
## cg21165486   83.0     86   93.0   90.0   35.5   95.0     87   79.0    0.0
## cg02736232   92.0     83   35.5   78.0   97.0   92.0      0   35.5   85.0
## cg13336515   35.5     35    0.0   72.0   83.0   35.5     80   82.0   86.0
## cg11515284   71.0     90   94.0   35.5   77.0   80.0     35   90.0   90.0
## cg26767974   99.0      0    0.0   94.0   78.0    0.0     91    0.0    0.0
## cg05627498   77.0     70    0.0   79.0   80.0   83.0     74   35.5    0.0
## cg09449232   87.0     89   86.0   35.5   94.0   81.0      0   83.0   78.0
## cg13176806    0.0      0    0.0   82.0   88.0   90.0     81   92.0    0.0
## cg14534405   35.5     35   85.0   35.5   91.0    0.0      0   93.0    0.0
## cg22364465   75.0      0    0.0   88.0   71.0   84.0      0   35.5    0.0
## cg15275625   35.5     94   81.0   35.5   92.0    0.0     86    0.0   84.0
## cg11743000    0.0     76   89.0   89.0   79.0   35.5      0   89.0    0.0
## cg04255401    0.0     35   75.0   35.5   35.5   86.0     71   35.5   88.0
## cg06749277   78.0     84    0.0    0.0   35.5   87.0     76    0.0    0.0
## cg27044597   81.0      0   73.0   35.5   74.0    0.0     35   75.0    0.0
## cg24196354   80.0     81   35.5   73.0    0.0   79.0     85    0.0    0.0
## cg03220447   91.0     35   35.5    0.0    0.0    0.0      0   74.0   83.0
## cg21533994   35.5     35   35.5    0.0   35.5   35.5     35    0.0   97.0
## cg03726357   35.5     35   78.0   35.5   35.5    0.0      0   86.0    0.0
## cg08248985   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg12451325   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg04934500   76.0      0    0.0   75.0   72.0   91.0      0   76.0    0.0
## cg07270865   72.0      0    0.0   81.0    0.0   35.5      0   35.5    0.0
## cg15630265   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg26079959   35.5     35   35.5   35.5   35.5    0.0      0    0.0   87.0
## cg13970113   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg13090941   35.5     35   71.0   35.5    0.0   35.5      0    0.0   72.0
## cg26445440   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg27539060    0.0     80    0.0   84.0   35.5   35.5     35    0.0    0.0
## cg19484548    0.0      0    0.0   93.0    0.0    0.0     35    0.0   35.5
## cg24486540    0.0     35   76.0   35.5    0.0   35.5      0    0.0   92.0
## cg09216797   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg00970981   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg24794608   35.5     78    0.0   35.5   35.5   94.0     35   72.0    0.0
## cg12164242   35.5     35    0.0   35.5   35.5   35.5     35   35.5    0.0
## cg03622758    0.0     35    0.0   35.5   35.5   35.5     35   35.5   35.5
##            iter38 iter39 iter40 iter41 iter42 iter43 iter44 iter45 iter46
## cg02988288  100.0  100.0  100.0  100.0     97     99   97.0  100.0     99
## cg02966936   95.0   99.0   87.0   87.0     86     97   95.0   91.0     92
## cg07175985   82.0   94.0   97.0   90.0     96     90  100.0   93.0     76
## cg14527110   35.5   96.0   96.0   91.0     94     73   99.0   96.0     95
## cg12220370   97.0   97.0   95.0   94.0     92     92   89.0   73.0     98
## cg13566279   83.0   82.0   90.0   92.0     93     88   98.0   97.0     97
## cg14490520   35.5   92.0   85.0   98.0     88     87   87.0   80.0     89
## cg03770217   92.0   95.0   94.0   99.0     76      0   94.0   90.0     96
## cg06184251   35.5   76.0   82.0   84.0     34      0   35.5   76.0     73
## cg25934997    0.0   84.0   71.0   35.5     34     96   84.0   94.0      0
## cg04577129   93.0   91.0   74.0   79.0     90     94   83.0   92.0     91
## cg25979005    0.0   85.0   92.0   72.0     71     91   86.0   95.0     35
## cg09467248   80.0   74.0   89.0   93.0     34     35   71.0   81.0     84
## cg17826980    0.0   98.0   35.5   86.0      0      0   35.5   71.0      0
## cg21165486   84.0   35.5   35.5   35.5     79     89   78.0   98.0     86
## cg02736232    0.0   90.0   35.5   35.5      0     82    0.0   35.5     35
## cg13336515    0.0   87.0   99.0   35.5     70     83   35.5   89.0      0
## cg11515284   35.5   35.5   98.0    0.0     95     98   72.0   35.5      0
## cg26767974   35.5   93.0   88.0   89.0     77     71   76.0   99.0     75
## cg05627498   96.0    0.0   93.0   88.0      0      0    0.0   82.0     85
## cg09449232   35.5    0.0   84.0   82.0     91     35   35.5   35.5     35
## cg13176806   89.0   78.0   35.5    0.0      0     95   90.0   77.0     71
## cg14534405   35.5   88.0   35.5   81.0     34      0   74.0    0.0     94
## cg22364465   35.5   73.0   35.5   35.5     84     35    0.0   87.0     35
## cg15275625   94.0   77.0   35.5   97.0     72      0    0.0   74.0     93
## cg11743000   99.0    0.0    0.0    0.0     81     93   93.0    0.0     81
## cg04255401   35.5   35.5   35.5    0.0     82     84   91.0   35.5      0
## cg06749277   87.0   83.0    0.0   78.0      0     70   80.0    0.0     90
## cg27044597    0.0   86.0   78.0   71.0      0     35    0.0    0.0      0
## cg24196354    0.0   72.0    0.0   83.0      0      0    0.0   85.0     82
## cg03220447    0.0   35.5    0.0   95.0     34      0    0.0   35.5     35
## cg21533994   35.5   35.5    0.0   75.0     34      0   35.5    0.0     35
## cg03726357    0.0   89.0   35.5   35.5     34      0   35.5   35.5      0
## cg08248985   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg12451325   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg04934500   71.0    0.0    0.0   35.5      0      0   79.0    0.0      0
## cg07270865    0.0   35.5   35.5   35.5      0     86    0.0   84.0      0
## cg15630265   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg26079959   35.5   35.5    0.0   85.0      0      0   35.5    0.0     35
## cg13970113   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg13090941    0.0   35.5   35.5    0.0     34      0    0.0    0.0      0
## cg26445440    0.0   35.5   35.5   35.5     34     35    0.0   35.5     35
## cg27539060    0.0   35.5    0.0   80.0      0      0    0.0   75.0     35
## cg19484548   91.0   81.0   79.0    0.0      0     35    0.0    0.0      0
## cg24486540   35.5    0.0   35.5    0.0     34     35   35.5   35.5      0
## cg09216797   35.5   35.5   35.5    0.0     34     35   35.5   35.5     35
## cg00970981   35.5   35.5    0.0   35.5     34     35   35.5   35.5     35
## cg24794608   35.5    0.0    0.0   74.0      0      0    0.0   35.5     35
## cg12164242    0.0   35.5   35.5    0.0     34     35    0.0   35.5     35
## cg03622758   35.5   35.5   35.5    0.0     34     35   35.5   35.5     35
##            iter47 iter48 iter49 iter50 iter51 iter52 iter53 iter54 iter55
## cg02988288     99  100.0  100.0   96.0  100.0   99.0  100.0  100.0  100.0
## cg02966936     95   81.0   98.0   97.0   79.0   96.0   96.0   94.0   96.0
## cg07175985     92   96.0   71.0   92.0   77.0   95.0   93.0   82.0   98.0
## cg14527110     97   88.0   82.0   88.0   98.0   98.0   98.0   96.0   97.0
## cg12220370     90   95.0   97.0   35.5   78.0   91.0   88.0   97.0   78.0
## cg13566279     98   99.0   99.0   35.5   94.0   35.5   92.0   98.0   86.0
## cg14490520     82   35.5   85.0   99.0   90.0   88.0   97.0   81.0   93.0
## cg03770217     96   83.0   35.5   78.0    0.0   78.0   90.0   71.0   82.0
## cg06184251     35    0.0   35.5   98.0   99.0   90.0   99.0   91.0   99.0
## cg25934997     78   85.0   91.0   77.0   92.0   83.0   91.0   76.0   95.0
## cg04577129     35   90.0   84.0    0.0   84.0   73.0    0.0   85.0    0.0
## cg25979005     35   94.0   79.0   83.0   35.5   35.5   73.0    0.0   80.0
## cg09467248     91    0.0   72.0    0.0   95.0    0.0   35.5   83.0   89.0
## cg17826980     35    0.0   74.0  100.0   89.0  100.0   94.0   77.0   91.0
## cg21165486     72   78.0   90.0    0.0   35.5    0.0   35.5   93.0   79.0
## cg02736232     75   35.5   95.0   84.0   82.0   97.0   89.0    0.0   72.0
## cg13336515     35   77.0    0.0   35.5   71.0   82.0   72.0   35.5   90.0
## cg11515284     84   74.0    0.0   35.5    0.0   93.0   35.5    0.0   35.5
## cg26767974      0   86.0   78.0   75.0   75.0   35.5   80.0   35.5    0.0
## cg05627498     94   87.0   81.0   72.0   83.0    0.0   71.0   90.0    0.0
## cg09449232     35    0.0    0.0    0.0   35.5   35.5   35.5   89.0   85.0
## cg13176806     89   84.0   88.0    0.0   76.0    0.0   84.0   84.0   84.0
## cg14534405     80   35.5    0.0    0.0   81.0   35.5   95.0   95.0   92.0
## cg22364465     86   93.0   96.0    0.0   93.0    0.0   86.0   87.0   71.0
## cg15275625     77   35.5    0.0    0.0   85.0    0.0    0.0   80.0    0.0
## cg11743000      0   91.0    0.0    0.0   96.0   35.5   87.0   99.0    0.0
## cg04255401      0    0.0    0.0   35.5    0.0   74.0   78.0   35.5   35.5
## cg06749277     85   79.0   89.0    0.0    0.0    0.0   79.0   88.0   77.0
## cg27044597      0    0.0    0.0   76.0    0.0   35.5   81.0    0.0   83.0
## cg24196354     70    0.0   80.0    0.0    0.0    0.0   35.5    0.0    0.0
## cg03220447     73    0.0    0.0   93.0   97.0   35.5   35.5   35.5   81.0
## cg21533994     35    0.0   35.5   94.0   91.0   35.5   74.0   35.5   35.5
## cg03726357     35    0.0    0.0   35.5    0.0   87.0   35.5    0.0   76.0
## cg08248985     35   35.5   35.5    0.0   35.5   35.5   35.5   35.5   35.5
## cg12451325     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg04934500     88   89.0   92.0    0.0    0.0    0.0    0.0    0.0    0.0
## cg07270865     79   80.0   75.0    0.0   74.0    0.0   35.5   35.5    0.0
## cg15630265     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg26079959     35    0.0    0.0   90.0   87.0    0.0   35.5   74.0   88.0
## cg13970113     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg13090941     35    0.0   35.5   73.0   35.5   86.0   82.0   35.5   35.5
## cg26445440     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg27539060     87   35.5   77.0    0.0    0.0    0.0    0.0    0.0    0.0
## cg19484548      0    0.0   35.5   87.0    0.0   81.0   76.0    0.0    0.0
## cg24486540      0   35.5   35.5   35.5   35.5   84.0   35.5   35.5   35.5
## cg09216797      0   35.5   35.5   35.5   35.5    0.0   35.5   35.5    0.0
## cg00970981     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg24794608      0    0.0   93.0   74.0    0.0   71.0    0.0    0.0    0.0
## cg12164242     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg03622758     35   35.5   35.5   35.5    0.0    0.0   35.5   35.5    0.0
##            iter56 iter57 iter58 iter59 iter60 iter61 iter62 iter63 iter64
## cg02988288   99.0  100.0  100.0     99  100.0  100.0     96     99   93.0
## cg02966936   82.0   90.0   98.0     92   97.0   86.0     97     91   90.0
## cg07175985   98.0   98.0   88.0     91   99.0   84.0     94     88  100.0
## cg14527110   91.0   97.0   99.0     93   92.0   99.0     98     94   96.0
## cg12220370   89.0   93.0   72.0     98   93.0   72.0     89     97   79.0
## cg13566279   95.0   95.0   89.0     87   87.0   90.0     95     90   91.0
## cg14490520  100.0   94.0   87.0     74   81.0   94.0     93     96   99.0
## cg03770217   86.0   96.0   92.0     76   98.0   73.0     75     80   86.0
## cg06184251   94.0   88.0   82.0      0   94.0   95.0     85     93   98.0
## cg25934997    0.0   77.0   95.0      0   96.0   85.0     84     85    0.0
## cg04577129    0.0   87.0   84.0     89    0.0   81.0     87     81    0.0
## cg25979005   74.0   83.0   35.5      0   35.5   79.0     92      0   74.0
## cg09467248   75.0   72.0   35.5      0   85.0   93.0     35     84   88.0
## cg17826980   88.0   35.5   35.5      0   73.0   96.0     99      0   94.0
## cg21165486   87.0   35.5   81.0     72   35.5   35.5     35     75    0.0
## cg02736232   76.0   78.0   83.0     35    0.0   77.0     90     35   84.0
## cg13336515   83.0   91.0    0.0     35   75.0   89.0     76      0   92.0
## cg11515284   72.0   99.0   93.0     78   82.0   78.0     35     95   85.0
## cg26767974   35.5    0.0   35.5     35   35.5   88.0     88      0    0.0
## cg05627498   71.0   76.0   79.0     80   84.0    0.0     81     74    0.0
## cg09449232    0.0   92.0   35.5     85   86.0   83.0     35     35   75.0
## cg13176806    0.0    0.0   90.0     88   89.0    0.0     86     83    0.0
## cg14534405   97.0   89.0    0.0     95   35.5   80.0      0      0   76.0
## cg22364465    0.0   82.0   75.0     90   35.5    0.0     91     35    0.0
## cg15275625   85.0   35.5   85.0     86   91.0   91.0      0     87   87.0
## cg11743000    0.0    0.0   80.0     96   88.0    0.0     78     98    0.0
## cg04255401   81.0   71.0   35.5     35   35.5    0.0      0     35   35.5
## cg06749277    0.0    0.0    0.0     83   35.5    0.0     79     82    0.0
## cg27044597   80.0   35.5   71.0      0   95.0   71.0     70     35   78.0
## cg24196354    0.0    0.0   35.5     35   90.0   76.0      0      0    0.0
## cg03220447   96.0   74.0   74.0     35    0.0   98.0     35      0   95.0
## cg21533994   90.0   35.5    0.0      0   77.0   82.0      0     35   35.5
## cg03726357   79.0   84.0   35.5     35   35.5   35.5     35     35   82.0
## cg08248985   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg12451325   35.5   35.5   35.5     35   35.5    0.0     35     35    0.0
## cg04934500    0.0    0.0    0.0      0   35.5    0.0      0      0    0.0
## cg07270865   35.5   35.5    0.0      0   35.5    0.0     74     35    0.0
## cg15630265    0.0   35.5   35.5     35   35.5    0.0     35     35    0.0
## cg26079959   84.0    0.0    0.0      0   76.0   75.0     35     35   81.0
## cg13970113    0.0    0.0   35.5     35   35.5    0.0     35     35    0.0
## cg13090941    0.0    0.0   35.5      0   78.0   35.5     73     35    0.0
## cg26445440    0.0   35.5   35.5     35   35.5    0.0     35     35    0.0
## cg27539060    0.0    0.0    0.0      0    0.0    0.0     80      0    0.0
## cg19484548    0.0    0.0   91.0     79   80.0    0.0     77     35    0.0
## cg24486540   35.5   79.0    0.0     35   35.5   35.5      0     73   35.5
## cg09216797    0.0    0.0   35.5     35   35.5    0.0     35     35   35.5
## cg00970981    0.0    0.0   35.5     35   35.5    0.0     35     35    0.0
## cg24794608    0.0    0.0    0.0     35   35.5    0.0     82      0    0.0
## cg12164242    0.0   35.5   35.5     35    0.0    0.0     35     35    0.0
## cg03622758    0.0    0.0   35.5     35    0.0   35.5     35     35    0.0
##            iter65 iter66 iter67 iter68 iter69 iter70 iter71 iter72 iter73
## cg02988288     98     99     98     95  100.0     99   98.0     99   99.0
## cg02966936     89     95     93     88   96.0     94   92.0     94   89.0
## cg07175985     96     94     89     98   95.0     97  100.0     84   96.0
## cg14527110     91     87     83     96   85.0     95   99.0     92   95.0
## cg12220370     88     97     87     89   98.0     98   97.0     96   85.0
## cg13566279     99     92     95     99   99.0     96   90.0     95    0.0
## cg14490520     94     96     96     97   94.0     93   95.0     35  100.0
## cg03770217     90     91     35     92   88.0     91   91.0     74    0.0
## cg06184251     95     83     82     35   35.5     74   87.0     35   97.0
## cg25934997     97     88     35     87   72.0     85   78.0     70   78.0
## cg04577129     92     84     92     85   90.0     90   88.0     91    0.0
## cg25979005     74     89     91     83   93.0     86   81.0     35    0.0
## cg09467248     76     75     84     94   83.0     83   35.5     82    0.0
## cg17826980     35     35     94     35    0.0     82   84.0     35   88.0
## cg21165486     35     93     90     71   35.5     72    0.0     89    0.0
## cg02736232     35      0     35     78   78.0     89   35.5     35   87.0
## cg13336515     73     35     85     73   35.5     35   93.0     85   35.5
## cg11515284     72      0     70     93   86.0     78   96.0     35   83.0
## cg26767974     35     86     99     35    0.0     81    0.0     88   35.5
## cg05627498     77     98     35      0   35.5      0   79.0     93    0.0
## cg09449232     93     35     79     35   35.5     35   89.0     98   93.0
## cg13176806     82     85      0      0   92.0     73    0.0      0    0.0
## cg14534405     87      0      0     91   87.0     75   86.0     72   92.0
## cg22364465     84     81      0     82   71.0     84   71.0     90    0.0
## cg15275625      0     70      0      0   82.0      0    0.0     73   73.0
## cg11743000     70      0     80      0   89.0     80    0.0     97    0.0
## cg04255401     35      0     88     74   80.0     88   77.0     78    0.0
## cg06749277     78     90      0      0   79.0      0    0.0     77    0.0
## cg27044597     35     82     35      0   35.5     35   85.0      0    0.0
## cg24196354     85     79     71      0   77.0     92   80.0      0    0.0
## cg03220447     35      0      0     35    0.0     35   35.5     35   98.0
## cg21533994     35     35     35      0    0.0      0   35.5      0   86.0
## cg03726357      0      0      0     35    0.0      0   82.0     35   91.0
## cg08248985     35     35     35     35   35.5     35   35.5     35    0.0
## cg12451325     35     35     35     35   35.5     35   35.5     35    0.0
## cg04934500     83     80      0     84   75.0      0    0.0      0    0.0
## cg07270865     35     73     86     35   35.5      0    0.0     35    0.0
## cg15630265     35     35     35     35   35.5     35   35.5     35    0.0
## cg26079959      0     35      0     35    0.0      0   75.0      0   74.0
## cg13970113     35     35     35     35   35.5     35   35.5     35    0.0
## cg13090941     35     35      0     35    0.0      0   35.5      0    0.0
## cg26445440     35     35     35      0   35.5     35   35.5     35    0.0
## cg27539060      0     35     76     86    0.0      0    0.0      0    0.0
## cg19484548     35      0     97      0   84.0      0    0.0     79    0.0
## cg24486540     35     35     35     35    0.0     35   35.5     35   35.5
## cg09216797     35     35     35     35   35.5     35    0.0     35    0.0
## cg00970981     35     35     35     35   35.5     35   35.5     35   35.5
## cg24794608     79      0     74      0   35.5     87    0.0      0    0.0
## cg12164242     35     35      0     35   35.5     35   35.5      0    0.0
## cg03622758     35     35     35     35   35.5     35   35.5     35    0.0
##            iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## cg02988288     98   97.0   99.0  100.0  100.0  100.0  100.0  100.0   97.0
## cg02966936     89   94.0  100.0   98.0   97.0   99.0   96.0   95.0   99.0
## cg07175985     99  100.0   97.0   91.0   85.0   87.0   92.0   97.0  100.0
## cg14527110     91   78.0   94.0   87.0   92.0   93.0   95.0   85.0   83.0
## cg12220370     92   90.0   79.0   96.0   99.0   96.0   94.0   98.0   91.0
## cg13566279     97   95.0   95.0   94.0   93.0   75.0   97.0   35.5   96.0
## cg14490520     96   89.0   90.0   99.0   86.0   98.0   81.0   35.5   95.0
## cg03770217     94   99.0   35.5    0.0   94.0   35.5   91.0   81.0   98.0
## cg06184251     83   35.5   89.0   97.0   95.0   92.0   35.5   35.5   35.5
## cg25934997     88   80.0   85.0   84.0   91.0    0.0   35.5   96.0   87.0
## cg04577129     85   98.0    0.0   85.0   87.0   88.0   90.0   76.0   94.0
## cg25979005     93   96.0   35.5    0.0   78.0    0.0   71.0   87.0   93.0
## cg09467248     81   84.0   74.0    0.0   73.0    0.0   82.0    0.0   80.0
## cg17826980     35   35.5   98.0   86.0   35.5   89.0    0.0   35.5   35.5
## cg21165486     35   91.0   96.0    0.0   35.5   84.0   35.5   93.0   71.0
## cg02736232     35   35.5   92.0   88.0   96.0   95.0    0.0   71.0   77.0
## cg13336515     76    0.0   78.0    0.0   35.5    0.0   72.0   84.0   35.5
## cg11515284      0    0.0   35.5   92.0   35.5   94.0   35.5    0.0   35.5
## cg26767974     95   85.0   84.0    0.0   89.0    0.0   89.0   92.0   89.0
## cg05627498     86   77.0   88.0    0.0   35.5   82.0    0.0   94.0    0.0
## cg09449232     35    0.0   35.5   76.0   35.5    0.0   80.0    0.0   35.5
## cg13176806     82   86.0   91.0   82.0   80.0    0.0   84.0   99.0   78.0
## cg14534405     87   35.5    0.0   90.0   83.0    0.0   85.0    0.0   35.5
## cg22364465      0    0.0   35.5    0.0    0.0   77.0   35.5   73.0    0.0
## cg15275625      0    0.0   86.0    0.0   35.5   79.0   93.0    0.0    0.0
## cg11743000     75   79.0    0.0   77.0    0.0    0.0   98.0    0.0   75.0
## cg04255401      0    0.0   82.0   35.5   35.5   72.0   83.0   35.5   35.5
## cg06749277     80   81.0   80.0   35.5    0.0    0.0    0.0   90.0    0.0
## cg27044597     70    0.0   81.0   83.0   35.5   85.0    0.0    0.0   79.0
## cg24196354      0    0.0   83.0   78.0   84.0    0.0   35.5    0.0   76.0
## cg03220447     35    0.0   35.5    0.0   98.0    0.0   35.5    0.0   35.5
## cg21533994     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg03726357     35    0.0   35.5   35.5   76.0    0.0   35.5    0.0   35.5
## cg08248985     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg12451325     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg04934500     78   92.0    0.0    0.0   82.0    0.0    0.0   35.5   81.0
## cg07270865     35   35.5   35.5    0.0    0.0    0.0   73.0   89.0    0.0
## cg15630265     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg26079959     35   35.5   71.0   35.5   77.0    0.0    0.0    0.0   35.5
## cg13970113     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg13090941      0   35.5   35.5    0.0   35.5   35.5    0.0    0.0   35.5
## cg26445440     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg27539060     84   87.0   35.5    0.0    0.0    0.0    0.0    0.0   88.0
## cg19484548      0   35.5   93.0    0.0    0.0   97.0   79.0   35.5    0.0
## cg24486540      0    0.0    0.0   35.5    0.0   35.5    0.0   35.5    0.0
## cg09216797     35   35.5   35.5   35.5   35.5    0.0   35.5   35.5   35.5
## cg00970981     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg24794608      0   76.0   72.0   75.0   90.0    0.0    0.0   86.0   82.0
## cg12164242     35    0.0    0.0   35.5   35.5   35.5   35.5   35.5   35.5
## cg03622758     35   35.5    0.0    0.0   35.5   35.5   35.5   35.5   35.5
##            iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## cg02988288     98  100.0  100.0   98.0   95.0  100.0  100.0  100.0   98.0
## cg02966936     94   95.0   98.0  100.0    0.0   96.0   99.0   91.0   92.0
## cg07175985     99   97.0   75.0   96.0   94.0   98.0   95.0   83.0   97.0
## cg14527110     91   84.0   35.5   95.0   99.0   94.0   92.0   98.0   76.0
## cg12220370     96   93.0   76.0   94.0   92.0   86.0   90.0   90.0  100.0
## cg13566279     95   98.0   95.0   97.0   84.0   95.0   93.0   97.0   99.0
## cg14490520     92   94.0   85.0   99.0   90.0   92.0   98.0   86.0   82.0
## cg03770217     93   83.0   74.0   86.0   73.0   99.0   91.0    0.0   96.0
## cg06184251     35   35.5   96.0   79.0   93.0   93.0   97.0   96.0   35.5
## cg25934997     83   85.0   99.0   93.0    0.0   97.0   94.0   94.0   75.0
## cg04577129     85   99.0    0.0   83.0    0.0    0.0   82.0    0.0   93.0
## cg25979005     87   96.0   84.0   92.0   83.0   84.0   35.5    0.0   79.0
## cg09467248     90   82.0   35.5   77.0   85.0   35.5   75.0   81.0   95.0
## cg17826980     35    0.0   92.0   90.0   35.5   35.5   80.0   88.0    0.0
## cg21165486     97   88.0   35.5   88.0   81.0    0.0   77.0   80.0   91.0
## cg02736232      0   90.0   97.0   74.0    0.0   35.5   35.5    0.0   35.5
## cg13336515     76   35.5   35.5   78.0  100.0   35.5   35.5   73.0    0.0
## cg11515284     35   86.0    0.0   85.0   97.0   73.0   87.0   95.0    0.0
## cg26767974     86   35.5   91.0   71.0   72.0   76.0    0.0   35.5   94.0
## cg05627498     74    0.0   93.0    0.0    0.0   35.5   76.0   35.5   89.0
## cg09449232     82   35.5    0.0   35.5   82.0   82.0   85.0   99.0    0.0
## cg13176806      0   87.0   90.0   76.0    0.0   90.0    0.0    0.0   87.0
## cg14534405     73   75.0    0.0   35.5   80.0   77.0   73.0   89.0   80.0
## cg22364465      0   73.0   72.0   89.0    0.0   35.5   84.0   74.0   35.5
## cg15275625     75   35.5   79.0    0.0    0.0    0.0    0.0    0.0   86.0
## cg11743000     79   78.0    0.0   81.0   87.0    0.0    0.0    0.0    0.0
## cg04255401     35   92.0    0.0   35.5   35.5   72.0   35.5   76.0   83.0
## cg06749277     81   35.5   80.0   84.0    0.0   79.0    0.0    0.0    0.0
## cg27044597     72    0.0   35.5   73.0    0.0   35.5   81.0   35.5   35.5
## cg24196354     77   81.0    0.0   72.0    0.0   91.0   96.0    0.0   35.5
## cg03220447     35    0.0   89.0   35.5    0.0   35.5   35.5   35.5   85.0
## cg21533994     35    0.0   35.5   35.5   74.0   35.5   35.5   35.5    0.0
## cg03726357     35    0.0    0.0   35.5   76.0   35.5   35.5   72.0   35.5
## cg08248985     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg12451325     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg04934500      0   79.0   87.0    0.0    0.0   81.0    0.0    0.0   88.0
## cg07270865     70   74.0   88.0   91.0   91.0    0.0    0.0    0.0    0.0
## cg15630265     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg26079959      0    0.0   35.5    0.0    0.0    0.0   35.5   35.5    0.0
## cg13970113     35   35.5   35.5   35.5   35.5    0.0   35.5   35.5   35.5
## cg13090941     35    0.0    0.0   35.5   35.5    0.0   79.0   92.0    0.0
## cg26445440     35   35.5    0.0   35.5   35.5    0.0   35.5   35.5   35.5
## cg27539060     89   89.0    0.0   35.5    0.0   71.0   71.0    0.0    0.0
## cg19484548      0    0.0    0.0   35.5    0.0   35.5    0.0    0.0    0.0
## cg24486540     35   35.5    0.0   35.5   98.0   35.5   35.5   35.5    0.0
## cg09216797     35   35.5    0.0   35.5   35.5   35.5    0.0   35.5   35.5
## cg00970981     35   35.5   35.5   35.5   35.5    0.0    0.0   35.5   35.5
## cg24794608      0   35.5   78.0    0.0    0.0   88.0    0.0    0.0   35.5
## cg12164242     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg03622758     35   35.5   35.5   35.5    0.0    0.0    0.0   35.5   35.5
##            iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## cg02988288  100.0  100.0  100.0   99.0  100.0   99.0   99.0   99.0      96
## cg02966936   97.0   81.0   95.0   98.0   99.0   95.0   90.0   97.0      97
## cg07175985   98.0   90.0   93.0  100.0   83.0  100.0   96.0   98.0      98
## cg14527110   99.0   92.0   98.0   96.0   85.0   98.0   80.0   90.0      74
## cg12220370   88.0   99.0   92.0   78.0   84.0   93.0   97.0   81.0      88
## cg13566279   94.0   83.0   94.0   75.0   92.0   96.0  100.0  100.0      92
## cg14490520   83.0   95.0   96.0   88.0   71.0   88.0   87.0   96.0      95
## cg03770217   95.0   96.0   83.0   83.0   82.0   86.0   77.0   35.5      90
## cg06184251   71.0   79.0   89.0   95.0   75.0   81.0   35.5   75.0      91
## cg25934997   93.0   71.0   78.0   93.0   98.0    0.0   86.0   92.0      99
## cg04577129   87.0   82.0   91.0    0.0    0.0   85.0   93.0   83.0      73
## cg25979005   96.0   91.0   76.0   87.0   94.0   82.0   79.0   82.0      82
## cg09467248   91.0   73.0   85.0    0.0   74.0   92.0   81.0   85.0      35
## cg17826980   75.0   74.0   87.0   72.0   35.5   84.0   35.5   94.0      72
## cg21165486   78.0   86.0    0.0    0.0   81.0   35.5   98.0    0.0      78
## cg02736232   76.0   72.0   35.5   86.0   80.0   75.0   35.5   80.0      93
## cg13336515   72.0   85.0    0.0   90.0   35.5   72.0   35.5   35.5      87
## cg11515284    0.0   35.5    0.0   89.0    0.0   35.5   35.5   76.0      85
## cg26767974   84.0   88.0   73.0    0.0   91.0   89.0   78.0    0.0      81
## cg05627498   74.0   84.0    0.0    0.0   90.0   35.5   35.5   89.0      84
## cg09449232    0.0    0.0   99.0   85.0   35.5   91.0   35.5   35.5       0
## cg13176806   80.0    0.0    0.0   91.0   96.0    0.0   71.0   91.0      75
## cg14534405    0.0   97.0   80.0   35.5    0.0   87.0    0.0    0.0      35
## cg22364465    0.0   93.0    0.0    0.0   72.0   74.0    0.0   84.0       0
## cg15275625    0.0   94.0   88.0    0.0   95.0   83.0   35.5    0.0       0
## cg11743000    0.0    0.0   97.0    0.0   78.0   77.0   35.5   35.5       0
## cg04255401    0.0   35.5   90.0   35.5    0.0   94.0   35.5   35.5       0
## cg06749277   81.0    0.0    0.0    0.0   88.0    0.0    0.0   79.0      79
## cg27044597   35.5   35.5    0.0   35.5   35.5    0.0   83.0   35.5      80
## cg24196354   85.0    0.0   86.0    0.0    0.0   78.0   85.0   35.5       0
## cg03220447    0.0   98.0    0.0   77.0   89.0    0.0    0.0    0.0      35
## cg21533994   35.5   76.0   35.5   35.5   35.5   35.5   35.5    0.0      35
## cg03726357   35.5    0.0   35.5   94.0    0.0   35.5    0.0   35.5       0
## cg08248985   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg12451325   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg04934500    0.0    0.0    0.0    0.0   79.0    0.0   35.5   87.0      83
## cg07270865   77.0   80.0    0.0    0.0   35.5    0.0   88.0   35.5      35
## cg15630265   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5      35
## cg26079959   35.5   35.5   35.5   35.5   35.5   35.5    0.0    0.0      35
## cg13970113   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5      35
## cg13090941   35.5    0.0   35.5   35.5    0.0   71.0   35.5   35.5      35
## cg26445440   35.5   35.5   35.5    0.0   35.5   35.5   35.5    0.0      35
## cg27539060   92.0    0.0   74.0    0.0    0.0   97.0   95.0    0.0      35
## cg19484548    0.0    0.0    0.0    0.0    0.0   90.0    0.0   72.0       0
## cg24486540    0.0   35.5    0.0   35.5    0.0    0.0   35.5   35.5       0
## cg09216797   35.5    0.0   35.5    0.0   35.5   35.5   35.5   35.5       0
## cg00970981    0.0   35.5   35.5    0.0   35.5    0.0   35.5   35.5      35
## cg24794608   35.5    0.0   79.0    0.0    0.0    0.0    0.0    0.0      89
## cg12164242   35.5   35.5   35.5    0.0   35.5    0.0   35.5   35.5      35
## cg03622758   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5      35
##            total_rank
## cg02988288     9869.0
## cg02966936     9199.5
## cg07175985     9174.0
## cg14527110     8944.0
## cg12220370     8809.0
## cg13566279     8784.5
## cg14490520     8553.0
## cg03770217     7394.0
## cg06184251     7085.5
## cg25934997     6932.0
## cg04577129     6741.5
## cg25979005     6649.0
## cg09467248     6635.0
## cg17826980     6001.5
## cg21165486     5770.5
## cg02736232     5648.5
## cg13336515     5626.5
## cg11515284     5624.5
## cg26767974     5576.5
## cg05627498     5532.5
## cg09449232     5456.5
## cg13176806     5374.5
## cg14534405     5002.5
## cg22364465     4913.0
## cg15275625     4719.5
## cg11743000     4659.0
## cg04255401     4424.0
## cg06749277     3999.5
## cg27044597     3919.5
## cg24196354     3664.0
## cg03220447     3657.5
## cg21533994     3566.5
## cg03726357     3483.5
## cg08248985     3396.0
## cg12451325     3289.5
## cg04934500     3196.0
## cg07270865     3082.0
## cg15630265     3076.5
## cg26079959     3037.5
## cg13970113     2934.5
## cg13090941     2887.0
## cg26445440     2828.5
## cg27539060     2782.5
## cg19484548     2755.0
## cg24486540     2746.0
## cg09216797     2723.0
## cg00970981     2686.5
## cg24794608     2680.5
## cg12164242     2651.5
## cg03622758     2650.5
write.table(meth_features_comp1_final,file="Comp1_METH_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")

meth_features_comp2_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),meth_features_comp2)
rownames(meth_features_comp2_final)<-meth_features_comp2_final$GENE
meth_features_comp2_final$GENE<-NULL
meth_features_comp2_final[is.na(meth_features_comp2_final)]<-0
meth_features_comp2_final$total_rank<-rowSums(meth_features_comp2_final)
meth_features_comp2_final<-meth_features_comp2_final[order(-meth_features_comp2_final$total_rank),]
print(head(meth_features_comp2_final,50))
##            iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## cg12451325  98.0  82.0  86.0  94.0  35.5   0.0  96.0  92.0  79.0     94
## cg08248985  94.0  35.5  35.5  90.0  76.0   0.0  84.0  87.0  78.0     92
## cg26445440  87.0  35.5  95.0  97.0  95.0   0.0  85.0  74.0  77.0     87
## cg12164242  95.0   0.0  98.0  98.0   0.0   0.0  88.0  84.0  81.0     97
## cg15630265  89.0   0.0  35.5  87.0  35.5   0.0  87.0  73.0  72.0     82
## cg09216797  96.0   0.0  78.0  78.0  97.0   0.0  90.0  83.0  85.0     73
## cg13544025  93.0   0.0  97.0 100.0   0.0   0.0  97.0  89.0  88.0     93
## cg27179424  97.0   0.0   0.0  99.0   0.0   0.0 100.0  97.0  89.0     95
## cg22152677   0.0  86.0  77.0   0.0  79.0   0.0  99.0  91.0  87.0      0
## cg12084792   0.0   0.0   0.0  96.0  77.0   0.0   0.0  75.0   0.0      0
## cg03622758  78.0   0.0  81.0  85.0   0.0   0.0  82.0  35.5  35.5     79
## cg14228710   0.0  35.5  85.0   0.0  72.0   0.0   0.0  79.0  35.5      0
## cg13559778  83.0   0.0  90.0  86.0   0.0   0.0  89.0  35.5   0.0     75
## cg07523470  75.0   0.0   0.0  92.0   0.0   0.0  92.0  35.5   0.0     78
## cg16901379  99.0   0.0   0.0   0.0   0.0   0.0   0.0  95.0  82.0     96
## cg08571304  81.0   0.0  76.0  84.0   0.0   0.0  75.0  35.5   0.0     84
## cg04684637  82.0   0.0   0.0  91.0   0.0   0.0  83.0  35.5  86.0     72
## cg12546646   0.0   0.0   0.0  76.0   0.0   0.0  71.0  35.5   0.0      0
## cg20836795  91.0   0.0  35.5  82.0  35.5   0.0  35.5  35.5   0.0     90
## cg20189782  71.0   0.0   0.0  77.0   0.0   0.0  93.0  35.5   0.0     71
## cg27051815   0.0   0.0   0.0   0.0  93.0   0.0   0.0   0.0 100.0      0
## cg00970981  85.0  35.5  35.5   0.0   0.0   0.0  78.0  35.5   0.0     80
## cg12747056 100.0   0.0   0.0   0.0   0.0   0.0   0.0  93.0  99.0      0
## cg02988288  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg14527110  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg02966936  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg07175985  35.5  35.5   0.0  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg12220370  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg14490520  35.5  35.5   0.0  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg13566279  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg06184251  35.5  35.5   0.0  35.5  35.5  35.5  35.5  35.5  35.5      0
## cg03770217  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5      0
## cg07935632   0.0   0.0   0.0   0.0   0.0   0.0   0.0  72.0  35.5     35
## cg14679463   0.0   0.0   0.0   0.0  35.5   0.0  79.0   0.0  35.5     91
## cg09467248  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5      0
## cg13336515  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg25979005  35.5  35.5  35.5  35.5  35.5   0.0  35.5  35.5  35.5     35
## cg13970113  35.5   0.0  35.5  35.5  35.5   0.0  35.5   0.0  35.5     35
## cg17826980  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5      0
## cg25934997  35.5  35.5   0.0   0.0   0.0   0.0  35.5  35.5  35.5      0
## cg02736232  35.5  35.5  35.5  35.5   0.0   0.0  35.5  35.5  35.5      0
## cg09449232  35.5  35.5   0.0  35.5  35.5  35.5  35.5  35.5   0.0     35
## cg11515284  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg21165486  35.5   0.0  35.5  35.5   0.0   0.0  35.5  35.5  35.5     35
## cg04577129  35.5   0.0  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg04255401   0.0  35.5   0.0  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg17802766   0.0  35.5   0.0   0.0  35.5   0.0   0.0  88.0  35.5      0
## cg21533994  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5  35.5     35
## cg26767974  35.5   0.0  35.5  35.5   0.0   0.0  35.5  35.5  35.5     35
## cg24486540  35.5  35.5  88.0   0.0   0.0  35.5  35.5  35.5   0.0     35
##            iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19
## cg12451325   74.0   98.0   35.5   88.0   87.0   96.0     70   94.0   80.0
## cg08248985   79.0   92.0    0.0   73.0   81.0   95.0     35   79.0   75.0
## cg26445440   75.0    0.0    0.0   90.0    0.0   89.0     87   92.0   92.0
## cg12164242   88.0    0.0   81.0   86.0   90.0   88.0     88   93.0    0.0
## cg15630265   35.5   97.0    0.0   93.0   35.5   85.0     74   84.0    0.0
## cg09216797   84.0   84.0    0.0    0.0   99.0   99.0     75   72.0   82.0
## cg13544025    0.0   99.0   87.0   84.0   93.0   97.0     92    0.0    0.0
## cg27179424   94.0    0.0   72.0   97.0   96.0   93.0     95    0.0    0.0
## cg22152677   96.0    0.0    0.0   95.0   92.0    0.0      0   85.0    0.0
## cg12084792   86.0    0.0    0.0   81.0   91.0   91.0     85   88.0    0.0
## cg03622758    0.0   90.0    0.0   35.5   79.0   80.0     35   75.0    0.0
## cg14228710   35.5    0.0   78.0   76.0    0.0    0.0     35   91.0   72.0
## cg13559778    0.0   82.0    0.0   35.5   78.0   81.0      0   86.0    0.0
## cg07523470   80.0   96.0    0.0  100.0   35.5   74.0      0    0.0    0.0
## cg16901379   95.0  100.0   76.0   91.0    0.0   98.0      0   97.0    0.0
## cg08571304   35.5   81.0    0.0   35.5   76.0   71.0      0   80.0    0.0
## cg04684637   35.5   87.0   77.0   83.0    0.0   94.0      0    0.0    0.0
## cg12546646    0.0   86.0    0.0    0.0   35.5   76.0      0   87.0    0.0
## cg20836795   35.5   95.0   35.5    0.0   80.0   86.0      0   73.0    0.0
## cg20189782    0.0   94.0    0.0   82.0    0.0   75.0      0    0.0    0.0
## cg27051815   99.0    0.0   97.0    0.0  100.0    0.0     97    0.0    0.0
## cg00970981   35.5   91.0    0.0    0.0    0.0   77.0      0   35.5    0.0
## cg12747056   85.0    0.0   85.0    0.0    0.0  100.0     91    0.0    0.0
## cg02988288   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg14527110   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg02966936   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg07175985   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg12220370   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg14490520   35.5    0.0   35.5   35.5    0.0   35.5     35   35.5   35.5
## cg13566279   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg06184251   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg03770217   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg07935632    0.0   93.0    0.0    0.0    0.0    0.0     35   76.0    0.0
## cg14679463   35.5    0.0   35.5   35.5   35.5    0.0     35   35.5    0.0
## cg09467248   35.5   35.5   35.5   35.5   35.5   35.5     35    0.0   35.5
## cg13336515   35.5    0.0   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg25979005   35.5   35.5   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg13970113    0.0   35.5    0.0   35.5   35.5   35.5     35   35.5    0.0
## cg17826980   35.5    0.0   35.5    0.0   35.5   35.5     35   35.5   35.5
## cg25934997   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg02736232   35.5    0.0    0.0   35.5   35.5   35.5     35    0.0   35.5
## cg09449232   35.5    0.0   35.5   35.5   35.5   35.5      0   35.5   35.5
## cg11515284   35.5    0.0   35.5    0.0    0.0   35.5      0   35.5   35.5
## cg21165486   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg04577129   35.5   35.5    0.0   35.5    0.0   35.5     35   35.5   35.5
## cg04255401   35.5    0.0    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg17802766   35.5    0.0   35.5   85.0    0.0    0.0     77    0.0   35.5
## cg21533994   35.5   35.5   35.5    0.0    0.0   35.5     35   35.5    0.0
## cg26767974   35.5   35.5    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg24486540   35.5   35.5   35.5    0.0    0.0   35.5     35   35.5   35.5
##            iter20 iter21 iter22 iter23 iter24 iter25 iter26 iter27 iter28
## cg12451325     83   91.0   99.0   88.0   75.0     77   78.0   90.0   79.0
## cg08248985     75   75.0   92.0   83.0   86.0     89   77.0   35.5   35.5
## cg26445440     70   93.0   96.0    0.0   97.0     73   88.0   85.0   95.0
## cg12164242     91   88.0    0.0   87.0   95.0     78    0.0   86.0   86.0
## cg15630265     35   80.0   98.0   79.0   89.0     72    0.0   76.0   78.0
## cg09216797     35   85.0   76.0   99.0    0.0      0   82.0   96.0    0.0
## cg13544025     89    0.0    0.0   85.0   88.0     86    0.0   94.0    0.0
## cg27179424     93   95.0   97.0   94.0   93.0     96   92.0   92.0    0.0
## cg22152677     72   90.0    0.0   89.0   76.0     87   35.5    0.0    0.0
## cg12084792     84   89.0   95.0   75.0   87.0     82    0.0   83.0   91.0
## cg03622758     35   35.5   79.0   72.0   35.5     35    0.0   73.0    0.0
## cg14228710     74   86.0    0.0    0.0    0.0     80   89.0   88.0   35.5
## cg13559778      0    0.0   94.0   81.0   35.5      0    0.0   84.0    0.0
## cg07523470      0    0.0   90.0    0.0   99.0      0    0.0    0.0    0.0
## cg16901379     88    0.0    0.0   93.0    0.0     90    0.0   95.0    0.0
## cg08571304      0    0.0   72.0   35.5   35.5      0    0.0   35.5   76.0
## cg04684637      0    0.0   84.0   86.0    0.0     35    0.0   80.0    0.0
## cg12546646     35   35.5   86.0   77.0   35.5     35    0.0   72.0   35.5
## cg20836795      0    0.0   87.0   71.0   35.5      0    0.0   35.5    0.0
## cg20189782      0    0.0   88.0   35.5   82.0     83    0.0    0.0    0.0
## cg27051815     94  100.0    0.0   98.0  100.0     92    0.0    0.0   96.0
## cg00970981     35    0.0   75.0   35.5    0.0     35   35.5   35.5   35.5
## cg12747056     95    0.0    0.0  100.0   92.0     81    0.0   98.0    0.0
## cg02988288     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg14527110     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg02966936     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg07175985     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg12220370     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg14490520     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg13566279     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg06184251     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg03770217     35   35.5   35.5   35.5   35.5     35   35.5   35.5    0.0
## cg07935632     35   77.0   93.0   80.0    0.0     35   35.5   87.0    0.0
## cg14679463     35   35.5    0.0   35.5   72.0     35    0.0    0.0   35.5
## cg09467248     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg13336515     35   35.5   35.5   35.5   35.5     35   35.5   35.5    0.0
## cg25979005     35   35.5   35.5   35.5   35.5     35   35.5   35.5    0.0
## cg13970113     35   35.5   35.5   35.5   35.5     35    0.0   35.5   35.5
## cg17826980     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg25934997     35   35.5   35.5   35.5   35.5     35   35.5   35.5   35.5
## cg02736232     35   35.5   35.5   35.5   35.5     35   35.5   35.5    0.0
## cg09449232     35   35.5   35.5   35.5   35.5      0   35.5   35.5   35.5
## cg11515284     35   35.5   35.5   35.5   35.5     35   35.5   35.5    0.0
## cg21165486     35   35.5   35.5   35.5   35.5      0    0.0    0.0    0.0
## cg04577129     35   35.5   35.5   35.5   35.5     35    0.0   35.5    0.0
## cg04255401     35   35.5   35.5   35.5   35.5      0   35.5   35.5   35.5
## cg17802766     71   83.0    0.0   35.5    0.0     35    0.0    0.0   35.5
## cg21533994     35    0.0   35.5   35.5    0.0     35   35.5    0.0   35.5
## cg26767974      0   35.5   35.5   35.5    0.0     35    0.0   35.5   35.5
## cg24486540     35    0.0   83.0    0.0   35.5     35   35.5   78.0    0.0
##            iter29 iter30 iter31 iter32 iter33 iter34 iter35 iter36 iter37
## cg12451325   90.0     79   92.0   99.0   93.0   97.0     74   92.0    0.0
## cg08248985   99.0     86   82.0   81.0   90.0   82.0     81   96.0   35.5
## cg26445440   77.0     96   89.0   96.0   86.0   95.0     89   79.0    0.0
## cg12164242   89.0     91    0.0   90.0   82.0   94.0     97   88.0    0.0
## cg15630265   88.0     85   80.0   35.5   87.0   90.0     90   73.0    0.0
## cg09216797   84.0     70   77.0   88.0   97.0   81.0     94   93.0    0.0
## cg13544025    0.0     95    0.0   87.0   83.0   89.0     98   86.0    0.0
## cg27179424    0.0     94    0.0    0.0   91.0   98.0     92   87.0    0.0
## cg22152677   95.0     87   95.0   94.0   94.0    0.0     93   99.0    0.0
## cg12084792    0.0     89    0.0   93.0   78.0   86.0     79   94.0    0.0
## cg03622758    0.0     82    0.0   83.0   81.0   72.0     87   35.5   72.0
## cg14228710   85.0     88   79.0    0.0   84.0    0.0      0   80.0   83.0
## cg13559778    0.0     73    0.0   84.0   35.5   84.0     35   35.5    0.0
## cg07523470    0.0     81    0.0   79.0   85.0   92.0     77    0.0    0.0
## cg16901379   92.0      0   94.0  100.0    0.0    0.0      0   97.0    0.0
## cg08571304   72.0     35    0.0   75.0   35.5   71.0     88   35.5    0.0
## cg04684637    0.0     71    0.0   35.5    0.0    0.0     78   84.0    0.0
## cg12546646   87.0     35    0.0   73.0   35.5    0.0      0   85.0    0.0
## cg20836795    0.0     72    0.0   80.0   35.5   85.0     75   82.0    0.0
## cg20189782   86.0     80    0.0   76.0    0.0    0.0      0   35.5    0.0
## cg27051815    0.0      0   96.0    0.0   96.0    0.0      0    0.0    0.0
## cg00970981   35.5     35   35.5   78.0   35.5   35.5     35   35.5    0.0
## cg12747056    0.0     97    0.0    0.0    0.0   99.0     99  100.0    0.0
## cg02988288   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg14527110   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg02966936   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg07175985   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg12220370   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg14490520   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg13566279   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg06184251   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg03770217   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg07935632   35.5      0   35.5   92.0   79.0    0.0      0   35.5   35.5
## cg14679463   35.5      0   75.0   71.0    0.0    0.0     83    0.0    0.0
## cg09467248   35.5     35   35.5   35.5   35.5    0.0     35   35.5   35.5
## cg13336515   35.5     35    0.0   35.5   35.5   35.5     35   35.5   35.5
## cg25979005   35.5     35    0.0   35.5   35.5   35.5     35    0.0    0.0
## cg13970113   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg17826980   35.5     35   35.5   35.5   35.5   35.5      0   35.5   35.5
## cg25934997   35.5     35    0.0   35.5   35.5   35.5      0   35.5    0.0
## cg02736232   35.5     35   35.5   35.5   35.5   35.5      0   35.5   35.5
## cg09449232   35.5     35   35.5   35.5   35.5   35.5      0   35.5   35.5
## cg11515284   35.5     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg21165486   35.5     35   35.5   35.5   35.5   35.5     35   35.5    0.0
## cg04577129   35.5     35   35.5   35.5   35.5   35.5     35    0.0   35.5
## cg04255401    0.0     35   35.5   35.5   35.5   35.5     35   35.5   35.5
## cg17802766   83.0      0   87.0   89.0   77.0    0.0      0   71.0    0.0
## cg21533994   35.5     35   35.5    0.0   35.5   35.5     35    0.0   35.5
## cg26767974   35.5      0    0.0   35.5   35.5    0.0     35    0.0    0.0
## cg24486540    0.0     35   35.5   35.5    0.0   35.5      0    0.0   35.5
##            iter38 iter39 iter40 iter41 iter42 iter43 iter44 iter45 iter46
## cg12451325   99.0   86.0   74.0   83.0     88     78   97.0   91.0     88
## cg08248985   35.5   81.0   35.5   86.0     82     70  100.0   72.0     92
## cg26445440    0.0   82.0   87.0   99.0     80     89    0.0   88.0     86
## cg12164242    0.0   93.0   78.0    0.0     87     86    0.0   83.0     98
## cg15630265   93.0   35.5   94.0   95.0     84     84   92.0   76.0     87
## cg09216797   82.0   92.0   88.0    0.0     71     74   87.0   87.0     90
## cg13544025    0.0   95.0   98.0    0.0     94     88   93.0   90.0     99
## cg27179424    0.0    0.0   92.0    0.0     91     99    0.0  100.0      0
## cg22152677    0.0   85.0   90.0    0.0     96     82   99.0   96.0      0
## cg12084792    0.0    0.0   35.5    0.0     90     93   96.0    0.0      0
## cg03622758   95.0   72.0   35.5    0.0     74     71   98.0   35.5     94
## cg14228710    0.0   84.0   84.0    0.0      0     94    0.0    0.0      0
## cg13559778   88.0   35.5   35.5    0.0     81     75   88.0   75.0     82
## cg07523470   87.0   90.0   91.0    0.0     85      0   81.0   85.0      0
## cg16901379    0.0    0.0    0.0    0.0     93     92    0.0   92.0      0
## cg08571304   98.0   35.5   72.0   75.0     70      0   84.0   74.0     83
## cg04684637    0.0    0.0   76.0    0.0     78      0   85.0   73.0      0
## cg12546646   94.0   35.5    0.0    0.0      0      0   94.0   79.0     97
## cg20836795   85.0    0.0   35.5    0.0     86     35   86.0   35.5     89
## cg20189782    0.0    0.0   35.5   93.0     69      0   74.0   71.0      0
## cg27051815    0.0  100.0   93.0  100.0      0      0    0.0    0.0      0
## cg00970981   35.5   35.5    0.0   35.5     34     35   82.0   35.5     70
## cg12747056    0.0    0.0   95.0    0.0     97      0    0.0   95.0      0
## cg02988288   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg14527110   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg02966936   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg07175985   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg12220370   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg14490520   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg13566279   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg06184251   35.5   35.5   35.5   35.5     34      0   35.5   35.5     35
## cg03770217   35.5   35.5   35.5   35.5     34      0   35.5   35.5     35
## cg07935632   75.0    0.0    0.0    0.0     34     35   79.0    0.0     81
## cg14679463    0.0   76.0   35.5   76.0     77      0    0.0   35.5      0
## cg09467248   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg13336515    0.0   35.5   35.5   35.5     34     35   35.5   35.5      0
## cg25979005    0.0   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg13970113   92.0   35.5   35.5   35.5     34     35   35.5   35.5     72
## cg17826980    0.0   35.5   35.5   35.5      0      0   35.5   35.5      0
## cg25934997    0.0   35.5   35.5   35.5     34     35   35.5   35.5      0
## cg02736232    0.0   35.5   35.5   35.5      0     35    0.0   35.5     35
## cg09449232   35.5    0.0   35.5   35.5     34     35   35.5   35.5     35
## cg11515284   35.5   35.5   35.5    0.0     34     35   35.5   35.5      0
## cg21165486   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg04577129   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg04255401   35.5   35.5   35.5    0.0     34     35   35.5   35.5      0
## cg17802766    0.0    0.0   79.0    0.0      0     83    0.0   89.0      0
## cg21533994   35.5   35.5    0.0   35.5     34      0   35.5    0.0     35
## cg26767974   35.5   35.5   35.5   35.5     34     35   35.5   35.5     35
## cg24486540   35.5    0.0   35.5    0.0     34     35   35.5   35.5      0
##            iter47 iter48 iter49 iter50 iter51 iter52 iter53 iter54 iter55
## cg12451325     94   96.0   99.0    0.0   35.5    0.0   85.0   90.0   84.0
## cg08248985     35   87.0   91.0    0.0   35.5   35.5   79.0   87.0   83.0
## cg26445440     99   82.0   93.0    0.0   83.0    0.0   97.0   77.0   92.0
## cg12164242     97   92.0   95.0    0.0   81.0    0.0   84.0   88.0   82.0
## cg15630265     92   77.0   94.0    0.0   72.0    0.0   74.0   78.0   35.5
## cg09216797      0   91.0   85.0   94.0   89.0    0.0   94.0   97.0    0.0
## cg13544025     98   94.0   90.0    0.0   90.0    0.0   89.0   96.0    0.0
## cg27179424     91   95.0   97.0    0.0   95.0    0.0   90.0   99.0    0.0
## cg22152677      0    0.0    0.0    0.0   78.0   82.0   86.0   95.0   95.0
## cg12084792     86    0.0    0.0    0.0   92.0    0.0   87.0    0.0   90.0
## cg03622758     78   86.0   35.5   35.5    0.0    0.0   76.0   73.0    0.0
## cg14228710      0    0.0    0.0    0.0   87.0   81.0   92.0   93.0   91.0
## cg13559778     81   90.0   96.0    0.0   76.0    0.0   78.0   35.5    0.0
## cg07523470     83   80.0   98.0    0.0    0.0    0.0   99.0   85.0    0.0
## cg16901379     88   93.0    0.0    0.0   75.0    0.0   95.0    0.0   86.0
## cg08571304     35   75.0   74.0    0.0   35.5    0.0   35.5   84.0    0.0
## cg04684637     82   88.0    0.0    0.0    0.0    0.0   82.0   74.0    0.0
## cg12546646      0   78.0   87.0    0.0   35.5    0.0   35.5   94.0   35.5
## cg20836795     70   81.0   72.0    0.0    0.0    0.0   35.5   35.5    0.0
## cg20189782     73   73.0    0.0    0.0   71.0    0.0   93.0   75.0    0.0
## cg27051815      0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   96.0
## cg00970981     35   35.5   76.0    0.0   35.5    0.0   35.5   92.0   35.5
## cg12747056      0  100.0    0.0    0.0    0.0    0.0    0.0    0.0   88.0
## cg02988288     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14527110     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg02966936     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg07175985     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg12220370     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14490520     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg13566279     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg06184251     35    0.0   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg03770217     35   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5
## cg07935632      0   89.0    0.0    0.0    0.0    0.0   75.0   76.0   85.0
## cg14679463     75    0.0    0.0    0.0    0.0    0.0   35.5    0.0    0.0
## cg09467248     35    0.0   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg13336515     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5   35.5
## cg25979005     35   35.5   35.5   35.5   35.5   35.5   35.5    0.0   35.5
## cg13970113     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg17826980     35    0.0   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg25934997     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg02736232     35   35.5   35.5   35.5   35.5   35.5   35.5    0.0   35.5
## cg09449232     35    0.0    0.0    0.0   35.5   35.5   35.5   35.5   35.5
## cg11515284     35   35.5    0.0   35.5    0.0   35.5   35.5    0.0   35.5
## cg21165486     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg04577129     35   35.5   35.5    0.0   35.5   35.5    0.0   35.5    0.0
## cg04255401      0    0.0    0.0   35.5    0.0   35.5   35.5   35.5   35.5
## cg17802766      0    0.0    0.0    0.0   35.5    0.0   81.0    0.0   35.5
## cg21533994     35    0.0   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg26767974      0   35.5   35.5   35.5   35.5   35.5   35.5   35.5    0.0
## cg24486540      0   35.5   71.0   35.5   35.5   35.5   35.5   35.5   35.5
##            iter56 iter57 iter58 iter59 iter60 iter61 iter62 iter63 iter64
## cg12451325   85.0   83.0   74.0     93   84.0    0.0     90     85    0.0
## cg08248985   78.0   35.5   35.5     80   78.0   35.5     86     84   96.0
## cg26445440    0.0   35.5   88.0     75   97.0    0.0     78     94    0.0
## cg12164242    0.0   88.0   77.0     85    0.0    0.0     91     97    0.0
## cg15630265    0.0   35.5   78.0     77   88.0    0.0     35     89    0.0
## cg09216797    0.0    0.0   96.0     92   87.0    0.0     79     88   99.0
## cg13544025    0.0   89.0   83.0     96    0.0    0.0     93      0    0.0
## cg27179424    0.0   97.0   93.0      0    0.0    0.0     99      0    0.0
## cg22152677    0.0   86.0   89.0      0    0.0    0.0      0     92    0.0
## cg12084792    0.0   35.5   86.0     90    0.0    0.0     96     99    0.0
## cg03622758    0.0    0.0   35.5     91    0.0   35.5     72     35    0.0
## cg14228710   84.0   71.0   92.0      0   92.0   35.5      0      0    0.0
## cg13559778    0.0   35.5   35.5     97    0.0    0.0     82     82    0.0
## cg07523470    0.0    0.0   95.0     72    0.0    0.0     92      0    0.0
## cg16901379    0.0   94.0   82.0      0    0.0    0.0     84     96    0.0
## cg08571304    0.0    0.0   75.0     35    0.0    0.0     35     81    0.0
## cg04684637    0.0   82.0    0.0     81    0.0    0.0      0     75    0.0
## cg12546646    0.0    0.0   85.0     88    0.0    0.0     71     95    0.0
## cg20836795    0.0   35.5   72.0     89    0.0    0.0     35     78    0.0
## cg20189782    0.0    0.0   73.0     79    0.0    0.0     88      0    0.0
## cg27051815    0.0   99.0    0.0      0    0.0   89.0     95      0    0.0
## cg00970981    0.0    0.0   35.5     83   71.0    0.0     35     76    0.0
## cg12747056    0.0   91.0   91.0      0    0.0   85.0     87     93    0.0
## cg02988288   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg14527110   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg02966936   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg07175985   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg12220370   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg14490520   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg13566279   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg06184251   35.5   35.5   35.5      0   35.5   35.5     35     35   35.5
## cg03770217   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg07935632    0.0   35.5    0.0     87   86.0    0.0     80     35    0.0
## cg14679463    0.0   35.5   35.5     82    0.0    0.0     35      0    0.0
## cg09467248   35.5   35.5   35.5      0   35.5   35.5     35     35   35.5
## cg13336515   35.5   35.5    0.0     35   35.5   35.5     35      0   35.5
## cg25979005   35.5   35.5   35.5      0   35.5   35.5     35      0   35.5
## cg13970113    0.0    0.0   35.5     35   35.5    0.0     35     35    0.0
## cg17826980   35.5   35.5   35.5      0   35.5   35.5     35      0   35.5
## cg25934997    0.0   35.5   35.5      0   35.5   35.5     35     35    0.0
## cg02736232   35.5   35.5   35.5     35    0.0   35.5     35     35   35.5
## cg09449232    0.0   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg11515284   35.5   35.5   35.5     35   35.5   35.5     35     35   35.5
## cg21165486   35.5   35.5   35.5     35   35.5   35.5     35     35    0.0
## cg04577129    0.0   35.5   35.5     35    0.0   35.5     35     35    0.0
## cg04255401   35.5   35.5   35.5     35   35.5    0.0      0     35   35.5
## cg17802766   87.0   35.5   76.0      0   83.0    0.0     75      0    0.0
## cg21533994   35.5   35.5    0.0      0   35.5   35.5      0     35   35.5
## cg26767974   35.5    0.0   35.5     35   35.5   35.5     35      0    0.0
## cg24486540   35.5   35.5    0.0     35   76.0   35.5      0     35   35.5
##            iter65 iter66 iter67 iter68 iter69 iter70 iter71 iter72 iter73
## cg12451325     97     87     90     97   95.0     92   85.0     91    0.0
## cg08248985     81     82     79     76   78.0     74   35.5     77    0.0
## cg26445440     93     97     81      0   92.0     35   79.0     81    0.0
## cg12164242     98     98      0     91   98.0     91   83.0      0    0.0
## cg15630265     96     92     76     73   35.5     82   73.0     94    0.0
## cg09216797     35     73     74     89   89.0     79    0.0     93    0.0
## cg13544025      0     94      0     90   96.0     98   84.0     97    0.0
## cg27179424      0      0     99     96   91.0     94   90.0     96    0.0
## cg22152677      0      0     87     94    0.0     88   92.0      0    0.0
## cg12084792     89      0     78     92  100.0      0   35.5     76    0.0
## cg03622758     35     84     35     74   80.0     35   35.5     78    0.0
## cg14228710      0      0     82      0   85.0      0   80.0     86    0.0
## cg13559778     35     78      0     78   76.0     35   35.5     73   35.5
## cg07523470     99     95      0     81    0.0     93    0.0      0    0.0
## cg16901379      0      0      0     95   94.0      0   93.0     90    0.0
## cg08571304     35     76      0     35   79.0     72   35.5     35    0.0
## cg04684637     94      0      0     79    0.0     71    0.0     88    0.0
## cg12546646     92      0     80     77   83.0     81    0.0     35    0.0
## cg20836795     85      0      0     84   82.0     35   35.5      0    0.0
## cg20189782     95     93      0     35   86.0     84    0.0     79    0.0
## cg27051815      0      0     98     99   99.0     99   98.0      0    0.0
## cg00970981     35     35     35     75   71.0     35   35.5     35   35.5
## cg12747056      0      0     97     98    0.0     89   89.0      0    0.0
## cg02988288     35     35     35     35   35.5     35   35.5     35   35.5
## cg14527110     35     35     35     35   35.5     35   35.5     35   35.5
## cg02966936     35     35     35     35   35.5     35   35.5     35   35.5
## cg07175985     35     35     35     35   35.5     35   35.5     35   35.5
## cg12220370     35     35     35     35   35.5     35   35.5     35   35.5
## cg14490520     35     35     35     35   35.5     35   35.5     35   35.5
## cg13566279     35     35     35     35   35.5     35   35.5     35    0.0
## cg06184251     35     35     35     35   35.5     35   35.5     35   35.5
## cg03770217     35     35     35     35   35.5     35   35.5     35    0.0
## cg07935632      0      0     35     82   81.0     35   72.0     74    0.0
## cg14679463      0     91     83      0   77.0     75   71.0      0    0.0
## cg09467248     35     35     35     35   35.5     35   35.5     35    0.0
## cg13336515     35     35     35     35   35.5     35   35.5     35   35.5
## cg25979005     35     35     35     35   35.5     35   35.5     35    0.0
## cg13970113     35     35     35     35   35.5     35   35.5     35    0.0
## cg17826980     35     35     35     35    0.0     35   35.5     35   35.5
## cg25934997     35     35     35     35   35.5     35   35.5     35   35.5
## cg02736232     35      0     35     35   35.5     35   35.5     35   35.5
## cg09449232     35     35     35     35   35.5     35   35.5     35   35.5
## cg11515284     35      0     35     35   35.5     35   35.5     35   35.5
## cg21165486     35     35     35     35   35.5     35    0.0     35    0.0
## cg04577129     35     35     35     35   35.5     35   35.5     35    0.0
## cg04255401     35      0     35     35   35.5     35   35.5     35    0.0
## cg17802766      0      0     94      0    0.0     83    0.0     87    0.0
## cg21533994     35     35     35      0    0.0      0   35.5      0   35.5
## cg26767974     35     35     35     35    0.0     35    0.0     35   35.5
## cg24486540     35     35     35     35    0.0     35   35.5     35   35.5
##            iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## cg12451325     82   94.0   35.5   35.5   78.0   79.0   87.0   91.0   90.0
## cg08248985     84   95.0   84.0   74.0   77.0   35.5   82.0   78.0   86.0
## cg26445440     90   97.0   89.0   94.0   76.0   94.0    0.0   95.0   96.0
## cg12164242     89    0.0    0.0   78.0   90.0   87.0   88.0   83.0   93.0
## cg15630265     87   84.0   35.5   83.0   75.0   80.0   35.5   94.0   88.0
## cg09216797     97   88.0   92.0   84.0   91.0    0.0   97.0   72.0   92.0
## cg13544025     93    0.0    0.0    0.0   96.0   96.0   94.0   88.0   94.0
## cg27179424     98  100.0  100.0    0.0    0.0   90.0   95.0    0.0    0.0
## cg22152677     99   96.0   76.0   86.0   80.0   82.0   96.0   86.0   99.0
## cg12084792     96   98.0    0.0    0.0    0.0   89.0   86.0   92.0   98.0
## cg03622758     74   73.0    0.0    0.0   84.0   35.5   85.0   35.5   79.0
## cg14228710     78    0.0   80.0   85.0   35.5   35.5    0.0   93.0   85.0
## cg13559778     35   35.5    0.0    0.0   83.0   81.0   79.0   35.5    0.0
## cg07523470     92   91.0    0.0    0.0    0.0   99.0   35.5   89.0    0.0
## cg16901379      0    0.0    0.0    0.0   92.0   85.0   92.0    0.0    0.0
## cg08571304     35   35.5    0.0    0.0    0.0   75.0   81.0    0.0   73.0
## cg04684637     73   79.0    0.0    0.0    0.0    0.0    0.0   81.0   91.0
## cg12546646      0   87.0   35.5    0.0    0.0   35.5   76.0    0.0   84.0
## cg20836795      0   74.0    0.0    0.0    0.0   35.5   83.0    0.0    0.0
## cg20189782      0   90.0    0.0    0.0    0.0   83.0   35.5    0.0   97.0
## cg27051815      0    0.0    0.0    0.0  100.0   97.0  100.0    0.0    0.0
## cg00970981     35   35.5   35.5   35.5   35.5   35.5   89.0   35.5   35.5
## cg12747056      0    0.0   90.0    0.0    0.0   92.0    0.0    0.0    0.0
## cg02988288     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14527110     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg02966936     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg07175985     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg12220370     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14490520     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg13566279     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg06184251     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg03770217     35   35.5   35.5    0.0   35.5   35.5   35.5   35.5   35.5
## cg07935632     83    0.0   35.5   35.5   35.5    0.0   35.5   75.0   78.0
## cg14679463     80   82.0    0.0    0.0    0.0    0.0   35.5   76.0   76.0
## cg09467248     35   35.5   35.5    0.0   35.5    0.0   35.5    0.0   35.5
## cg13336515     35    0.0   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg25979005     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg13970113     35   77.0   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg17826980     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg25934997     35   35.5   35.5   35.5   35.5    0.0   35.5   35.5   35.5
## cg02736232     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg09449232     35    0.0   35.5   35.5   35.5    0.0   35.5    0.0   35.5
## cg11515284      0    0.0   35.5   35.5   35.5   35.5   35.5    0.0   35.5
## cg21165486     35   35.5   35.5    0.0   35.5   35.5   35.5   35.5   35.5
## cg04577129     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5   35.5
## cg04255401      0    0.0   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg17802766      0    0.0   35.5    0.0    0.0    0.0    0.0    0.0    0.0
## cg21533994     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg26767974     35   35.5   35.5    0.0   35.5    0.0   35.5   35.5   35.5
## cg24486540      0    0.0    0.0   35.5    0.0   35.5    0.0   74.0    0.0
##            iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## cg12451325     92   98.0   91.0   87.0   95.0   82.0   91.0   91.0   92.0
## cg08248985     90   88.0   35.5   86.0   94.0   35.5   73.0   78.0   87.0
## cg26445440     98   92.0    0.0   83.0   78.0    0.0   98.0   92.0   91.0
## cg12164242     86   97.0   85.0   93.0   85.0   92.0   89.0   88.0   94.0
## cg15630265     91   85.0   96.0   92.0   84.0   35.5   79.0   75.0   93.0
## cg09216797     88   80.0    0.0   79.0   75.0   87.0    0.0   87.0   89.0
## cg13544025     94   91.0   92.0    0.0    0.0   94.0    0.0   77.0   97.0
## cg27179424     97  100.0   86.0   90.0    0.0    0.0   97.0   95.0   99.0
## cg22152677     93   95.0    0.0   96.0    0.0   93.0   88.0   83.0    0.0
## cg12084792      0   99.0    0.0   95.0    0.0    0.0    0.0    0.0   88.0
## cg03622758     80   86.0   35.5   71.0    0.0    0.0    0.0   35.5   82.0
## cg14228710      0    0.0   35.5   84.0   98.0   75.0   86.0   79.0    0.0
## cg13559778     76   77.0   35.5   35.5    0.0    0.0    0.0   35.5   76.0
## cg07523470     95   96.0    0.0   97.0    0.0    0.0    0.0   81.0   86.0
## cg16901379     96    0.0   87.0    0.0    0.0   95.0    0.0   93.0    0.0
## cg08571304     83   76.0   35.5   35.5    0.0    0.0   35.5   35.5   80.0
## cg04684637     89   75.0   35.5   35.5   74.0   81.0    0.0   72.0   81.0
## cg12546646      0    0.0   73.0   88.0   86.0    0.0   72.0   74.0   85.0
## cg20836795      0   74.0    0.0   82.0    0.0   35.5    0.0   35.5   35.5
## cg20189782     78   83.0    0.0   76.0    0.0   74.0    0.0   35.5   96.0
## cg27051815      0    0.0   99.0  100.0    0.0   99.0   99.0    0.0    0.0
## cg00970981     72   35.5   35.5   72.0   35.5    0.0    0.0   35.5   35.5
## cg12747056     99    0.0    0.0    0.0   92.0    0.0    0.0    0.0    0.0
## cg02988288     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14527110     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg02966936     35   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5
## cg07175985     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg12220370     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg14490520     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg13566279     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg06184251     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg03770217     35   35.5   35.5   35.5   35.5   35.5   35.5    0.0   35.5
## cg07935632      0   81.0    0.0    0.0   35.5    0.0    0.0    0.0    0.0
## cg14679463     84    0.0    0.0   74.0   76.0   35.5   77.0   35.5   84.0
## cg09467248     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5
## cg13336515     35   35.5   35.5   35.5   35.5   35.5   35.5   35.5    0.0
## cg25979005     35   35.5   35.5   35.5   35.5   35.5   35.5    0.0   35.5
## cg13970113     35   35.5   35.5   35.5   35.5    0.0   35.5   35.5   35.5
## cg17826980     35    0.0   35.5   35.5   35.5   35.5   35.5   35.5    0.0
## cg25934997     35   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5
## cg02736232      0   35.5   35.5   35.5    0.0   35.5   35.5    0.0   35.5
## cg09449232     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5    0.0
## cg11515284     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5    0.0
## cg21165486     35   35.5   35.5   35.5   35.5    0.0   35.5   35.5   35.5
## cg04577129     35   35.5    0.0   35.5    0.0    0.0   35.5    0.0   35.5
## cg04255401     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5   35.5
## cg17802766      0    0.0   89.0    0.0   89.0   76.0   82.0   89.0    0.0
## cg21533994     35    0.0   35.5   35.5   35.5   35.5   35.5   35.5    0.0
## cg26767974     35   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5
## cg24486540     35   35.5    0.0   35.5   35.5   35.5   35.5   35.5    0.0
##            iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## cg12451325   77.0   74.0   93.0   74.0   71.0   87.0   95.0   94.0      98
## cg08248985   85.0   81.0   83.0   35.5   89.0   77.0   94.0   93.0      86
## cg26445440   97.0   78.0   35.5    0.0   85.0   79.0   97.0    0.0      82
## cg12164242   86.0   94.0   88.0    0.0   86.0    0.0   99.0   92.0      92
## cg15630265   76.0   35.5   85.0    0.0   81.0   72.0   80.0   88.0      95
## cg09216797   91.0    0.0   95.0    0.0   97.0   92.0   83.0   80.0       0
## cg13544025   92.0   93.0   79.0    0.0   91.0   94.0   92.0   97.0      97
## cg27179424   99.0   84.0   97.0    0.0   96.0   93.0  100.0   98.0       0
## cg22152677    0.0    0.0   99.0   35.5   92.0   98.0   98.0   87.0      85
## cg12084792   88.0   98.0   81.0    0.0   88.0   35.5   85.0   90.0       0
## cg03622758   35.5   35.5   73.0    0.0   83.0   73.0   35.5   82.0      74
## cg14228710   73.0   75.0   80.0   79.0    0.0   35.5    0.0    0.0      89
## cg13559778   35.5    0.0   76.0    0.0   35.5    0.0   84.0   81.0      75
## cg07523470   90.0   85.0   35.5    0.0   76.0    0.0   88.0   35.5      90
## cg16901379   81.0   79.0    0.0    0.0   90.0   84.0    0.0   99.0      94
## cg08571304   35.5   35.5   35.5    0.0   35.5   35.5   77.0   35.5      35
## cg04684637   35.5   35.5   35.5    0.0   77.0    0.0   78.0   89.0      93
## cg12546646    0.0   35.5   84.0    0.0    0.0   35.5    0.0   83.0      87
## cg20836795    0.0   35.5   75.0    0.0   35.5   85.0   81.0   84.0      35
## cg20189782   89.0    0.0   35.5    0.0   78.0    0.0   86.0   78.0       0
## cg27051815   96.0   95.0  100.0    0.0    0.0   96.0    0.0    0.0      96
## cg00970981    0.0   35.5   71.0    0.0   72.0    0.0   74.0   35.5      35
## cg12747056    0.0    0.0   94.0    0.0   93.0   99.0    0.0  100.0       0
## cg02988288   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg14527110   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg02966936   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg07175985   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg12220370   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg14490520   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg13566279   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg06184251   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg03770217   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg07935632   35.5    0.0   82.0    0.0    0.0   35.5    0.0   86.0      35
## cg14679463   35.5   72.0   72.0    0.0   35.5   35.5   93.0   75.0       0
## cg09467248   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5      35
## cg13336515   35.5   35.5    0.0   35.5   35.5   35.5   35.5   35.5      35
## cg25979005   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg13970113   35.5   35.5   35.5    0.0   35.5   35.5   35.5   35.5      35
## cg17826980   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg25934997   35.5   35.5   35.5   35.5   35.5    0.0   35.5   35.5      35
## cg02736232   35.5   35.5   35.5   35.5   35.5   35.5   35.5   35.5      35
## cg09449232    0.0    0.0   35.5   35.5   35.5   35.5   35.5   35.5       0
## cg11515284    0.0   35.5    0.0   35.5    0.0   35.5   35.5   35.5      35
## cg21165486   35.5   35.5    0.0    0.0   35.5   35.5   35.5    0.0      35
## cg04577129   35.5   35.5   35.5    0.0    0.0   35.5   35.5   35.5      35
## cg04255401    0.0   35.5   35.5   35.5    0.0   35.5   35.5   35.5       0
## cg17802766    0.0    0.0    0.0    0.0   35.5    0.0    0.0    0.0      83
## cg21533994   35.5   35.5   35.5   35.5   35.5   35.5   35.5    0.0      35
## cg26767974   35.5   35.5   35.5    0.0   35.5   35.5   35.5    0.0      35
## cg24486540    0.0   35.5    0.0   35.5    0.0    0.0   35.5   35.5       0
##            total_rank
## cg12451325     7904.5
## cg08248985     7125.5
## cg26445440     6837.5
## cg12164242     6692.0
## cg15630265     6592.5
## cg09216797     6562.0
## cg13544025     6164.0
## cg27179424     5956.0
## cg22152677     5548.0
## cg12084792     4928.0
## cg03622758     4780.0
## cg14228710     4440.5
## cg13559778     4328.5
## cg07523470     4313.5
## cg16901379     4287.0
## cg08571304     4010.5
## cg04684637     3898.0
## cg12546646     3867.0
## cg20836795     3731.5
## cg20189782     3718.5
## cg27051815     3700.0
## cg00970981     3618.5
## cg12747056     3568.0
## cg02988288     3538.0
## cg14527110     3538.0
## cg02966936     3502.5
## cg07175985     3502.5
## cg12220370     3502.5
## cg14490520     3431.5
## cg13566279     3396.0
## cg06184251     3362.0
## cg03770217     3219.5
## cg07935632     3202.0
## cg14679463     3196.0
## cg09467248     3077.5
## cg13336515     3077.5
## cg25979005     3077.5
## cg13970113     3069.5
## cg17826980     3010.0
## cg25934997     2936.5
## cg02736232     2902.5
## cg09449232     2865.5
## cg11515284     2865.5
## cg21165486     2864.0
## cg04577129     2828.0
## cg04255401     2760.5
## cg17802766     2732.5
## cg21533994     2724.5
## cg26767974     2688.0
## cg24486540     2662.0
write.table(meth_features_comp2_final,file="Comp2_METH_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")


gen_features_comp1_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),gen_features_comp1)
rownames(gen_features_comp1_final)<-gen_features_comp1_final$GENE
gen_features_comp1_final$GENE<-NULL
gen_features_comp1_final[is.na(gen_features_comp1_final)]<-0
gen_features_comp1_final$total_rank<-rowSums(gen_features_comp1_final)
gen_features_comp1_final<-gen_features_comp1_final[order(-gen_features_comp1_final$total_rank),]
print(head(gen_features_comp1_final,50))
##              iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## rs13279576_A    18     0  40.0    39    39  17.5     0    40    18     18
## rs7931183_A      0    39   0.0    40    40  39.0    18    37    40      0
## rs10837562_G    18    18   0.0    37    18  36.0    38    36    37     40
## rs9296990_A     40     0   0.0    18    38  17.5    18     0    18     18
## rs2331992_G     39     0   0.0     0     0   0.0    39     0    18     18
## rs12476415_G    18    18  35.5     0    18   0.0     0    18     0     18
## rs7430710_A     18     0  17.5    18     0  17.5    18     0    18     18
## rs11257386_A    18     0   0.0    18    18   0.0    40     0     0      0
## rs2149733_A     18     0   0.0    18     0  17.5     0    18     0     18
## rs2168477_A     18     0  17.5    18     0  17.5     0    18     0      0
## rs10412466_G    18     0  39.0     0     0  35.0     0    18     0     38
## rs694625_A       0    18  17.5    18    36   0.0     0     0     0     18
## rs11150589_A    18    18  17.5    18    18   0.0    18     0     0      0
## rs16825228_G     0     0  35.5     0    18   0.0     0     0     0     18
## rs12005670_A    18    18   0.0    18     0   0.0     0    18    18      0
## rs12598978_A    18    18  17.5    18     0   0.0    18     0     0      0
## rs10405944_G     0     0  17.5     0    18  17.5     0     0    18     18
## rs12629469_A     0     0  38.0    18    37   0.0     0    18     0      0
## rs10063667_A     0    18   0.0     0    18  17.5    18    18     0      0
## rs8093884_A     18    37   0.0    18    18   0.0    18     0     0      0
## rs10795908_G     0    38   0.0     0     0   0.0    18     0     0      0
## rs4821456_G     18     0   0.0     0     0   0.0     0     0     0     36
## rs13058433_G     0    18   0.0     0     0   0.0     0     0    38      0
## rs7807747_A      0     0   0.0    18     0   0.0    18    18     0     18
## rs12679812_A     0     0   0.0     0     0   0.0     0    18    18      0
## rs11981000_G     0     0   0.0     0    18   0.0    18    18    18      0
## rs10050725_G     0     0   0.0     0     0  38.0     0    38     0     18
## rs4716858_A      0    40   0.0     0     0   0.0     0     0    39     18
## rs7840855_G     18     0  17.5     0     0  17.5    18     0    18      0
## rs818441_A      18     0  17.5    18     0  17.5     0     0    18      0
## rs4282978_A      0     0  17.5     0     0   0.0     0    39     0      0
## rs635070_A       0     0   0.0     0     0   0.0    18     0    18      0
## rs12794303_G     0     0   0.0     0     0  17.5     0     0     0     18
## rs9291002_A      0     0   0.0     0     0  17.5    18     0     0      0
## rs4932545_G      0     0   0.0    18     0   0.0    18    18     0      0
## rs742502_G       0    18   0.0     0     0   0.0     0     0    18      0
## rs1016090_G      0     0   0.0     0     0   0.0     0     0     0      0
## rs1732581_A     18     0  17.5     0     0   0.0     0     0     0      0
## rs750064_G       0     0  17.5     0     0   0.0     0     0     0      0
## rs4961252_G      0     0   0.0     0    18  17.5     0     0     0      0
## rs933881_G       0     0   0.0     0    18  17.5     0     0     0     18
## rs7327037_G      0     0   0.0     0    18   0.0     0     0     0      0
## rs1882153_A     18     0  17.5     0     0   0.0     0     0     0      0
## rs2835695_G      0     0   0.0     0     0   0.0    18     0     0      0
## rs11218557_G    18     0  17.5    18     0   0.0     0     0     0      0
## rs3095301_G      0    18   0.0     0     0   0.0    18     0     0      0
## rs3095302_A      0    18   0.0     0     0   0.0    18     0     0      0
## rs3131003_A      0    18   0.0     0     0   0.0    18     0     0      0
## rs7725574_C      0    18   0.0     0     0   0.0     0     0    36      0
## rs7912364_C      0     0  17.5    18     0  17.5     0     0     0     18
##              iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18
## rs13279576_A     36     18     18     40     39     18     36     40
## rs7931183_A      18      0     40     39     18     38     37      0
## rs10837562_G     40     38     18      0      0     39     40     18
## rs9296990_A       0     40     39     18      0     18     18     18
## rs2331992_G       0     18      0     18      0      0      0      0
## rs12476415_G     18     36      0     18      0      0     18      0
## rs7430710_A      18     18     18     18     18     18     18     18
## rs11257386_A      0      0      0      0      0      0     18      0
## rs2149733_A      18      0     18      0     18     18     18     18
## rs2168477_A      18     18      0     18     18      0      0      0
## rs10412466_G      0      0     18      0      0      0      0     36
## rs694625_A        0     37      0      0      0      0     18      0
## rs11150589_A     18      0     18      0      0     18     18      0
## rs16825228_G      0     18      0     18      0      0     18      0
## rs12005670_A     18     18      0     18      0     18     18      0
## rs12598978_A     18      0     18      0      0     18     18      0
## rs10405944_G     18      0      0      0      0     18      0     18
## rs12629469_A     18     39      0      0      0      0      0      0
## rs10063667_A     18      0     18      0     18     18     18     18
## rs8093884_A       0     18      0     18      0     18      0      0
## rs10795908_G      0      0      0      0      0      0     18      0
## rs4821456_G       0      0      0      0      0      0     18      0
## rs13058433_G     18      0      0     18      0      0     18     18
## rs7807747_A       0      0      0      0      0      0     18      0
## rs12679812_A     18      0      0     18      0      0     18      0
## rs11981000_G      0      0      0      0     40     40     38      0
## rs10050725_G     18      0     18      0      0     18     18     18
## rs4716858_A       0      0      0      0      0      0     18      0
## rs7840855_G       0      0      0      0      0     18      0     18
## rs818441_A       18      0      0     18      0      0     18      0
## rs4282978_A       0      0      0      0      0      0     18     37
## rs635070_A        0      0      0     18      0      0      0      0
## rs12794303_G     18     18      0     18      0      0     18      0
## rs9291002_A       0      0      0      0      0      0     18      0
## rs4932545_G       0      0      0      0      0     18     18      0
## rs742502_G        0      0      0     18      0      0      0     18
## rs1016090_G       0      0     18      0     18      0     18     18
## rs1732581_A       0      0      0      0      0     18      0      0
## rs750064_G        0      0      0      0      0      0      0      0
## rs4961252_G       0      0      0     18     36      0      0      0
## rs933881_G        0      0      0      0      0      0      0      0
## rs7327037_G       0      0      0      0      0      0      0      0
## rs1882153_A       0      0      0      0      0     18      0      0
## rs2835695_G       0      0     18      0     38     18      0      0
## rs11218557_G      0      0      0      0     18      0      0      0
## rs3095301_G       0     18      0      0      0      0      0      0
## rs3095302_A       0     18      0      0      0      0      0      0
## rs3131003_A       0     18      0      0      0      0      0      0
## rs7725574_C       0      0      0      0      0      0      0      0
## rs7912364_C       0      0      0      0      0     18      0     18
##              iter19 iter20 iter21 iter22 iter23 iter24 iter25 iter26
## rs13279576_A     18     38     39     36     18     18      0    0.0
## rs7931183_A      18     18     18     40     40     18     18   36.0
## rs10837562_G     38     18      0      0      0      0     36    0.0
## rs9296990_A      39      0      0     18      0      0     18    0.0
## rs2331992_G      18      0     18     39      0      0      0    0.0
## rs12476415_G      0     18     37      0     18     40     18    0.0
## rs7430710_A      18     18     18      0     18     18     18   18.0
## rs11257386_A     37     18     40     37     18     36      0    0.0
## rs2149733_A      18     18     18      0      0     18     18    0.0
## rs2168477_A       0      0     18     18      0     18     18    0.0
## rs10412466_G      0     37      0      0     18      0      0    0.0
## rs694625_A        0     40     18     38     18     18      0    0.0
## rs11150589_A     18      0     18     18      0      0      0    0.0
## rs16825228_G      0     18     18      0      0     39     18    0.0
## rs12005670_A      0      0     18     18     18      0     18    0.0
## rs12598978_A     18      0     18     18      0      0      0    0.0
## rs10405944_G     18      0      0     18      0     18     18   18.0
## rs12629469_A     18      0      0      0      0      0      0    0.0
## rs10063667_A     18     18      0      0      0      0     18    0.0
## rs8093884_A       0      0      0     18     18      0     18   18.0
## rs10795908_G     18     18     38      0     18     18      0    0.0
## rs4821456_G       0     18      0      0      0      0      0    0.0
## rs13058433_G      0     36      0     18      0      0      0   39.5
## rs7807747_A       0     18     18     18      0      0      0   18.0
## rs12679812_A     18      0      0      0      0      0     18    0.0
## rs11981000_G      0     18      0      0     39      0      0    0.0
## rs10050725_G     18      0      0      0     18      0      0    0.0
## rs4716858_A      18     39      0      0      0      0      0    0.0
## rs7840855_G       0      0      0      0      0      0     18   18.0
## rs818441_A        0     18      0      0      0     18     18    0.0
## rs4282978_A       0     18     18      0      0      0      0    0.0
## rs635070_A        0      0      0     18      0     18     18   18.0
## rs12794303_G     18     18     18      0     18     18      0    0.0
## rs9291002_A       0     18      0     18      0      0      0    0.0
## rs4932545_G       0      0      0     18      0      0     18    0.0
## rs742502_G        0     18      0     18      0      0      0   39.5
## rs1016090_G       0      0      0      0      0      0      0    0.0
## rs1732581_A       0     18     18      0      0     18      0    0.0
## rs750064_G        0      0     36     18     18      0      0    0.0
## rs4961252_G      18      0      0      0      0     18      0   18.0
## rs933881_G       40      0      0      0      0      0      0    0.0
## rs7327037_G      36      0      0      0      0     18      0   18.0
## rs1882153_A       0     18     18      0      0      0      0    0.0
## rs2835695_G       0      0      0     18      0      0      0    0.0
## rs11218557_G      0     18     18      0      0      0      0    0.0
## rs3095301_G       0      0      0      0     18      0      0   18.0
## rs3095302_A       0      0      0      0     18      0      0   18.0
## rs3131003_A       0      0      0      0     18      0      0   18.0
## rs7725574_C       0      0      0      0      0      0     18    0.0
## rs7912364_C       0      0      0      0      0     18      0    0.0
##              iter27 iter28 iter29 iter30 iter31 iter32 iter33 iter34
## rs13279576_A      0     38     40     18     37     40     39      0
## rs7931183_A      40     39      0     40     18      0     36     39
## rs10837562_G      0     18     18     39     40     37      0     18
## rs9296990_A       0      0     18     38     18     39      0     18
## rs2331992_G      38      0      0      0      0     18      0     40
## rs12476415_G      0     18      0      0      0     18     18      0
## rs7430710_A       0      0     18     18     18     18      0     18
## rs11257386_A     18      0     18     18      0      0     18     37
## rs2149733_A       0     18     18     18     18     18     18     18
## rs2168477_A       0     18     18     18     18     18     18     18
## rs10412466_G     18      0      0     36     18     18     18     18
## rs694625_A        0      0     18      0      0      0     40     18
## rs11150589_A      0     18     18     18      0      0     18      0
## rs16825228_G      0      0      0      0      0     18      0      0
## rs12005670_A      0      0     18      0      0     18     18     18
## rs12598978_A      0     18     18     18      0      0     18      0
## rs10405944_G     18     18      0      0      0     18     18     18
## rs12629469_A      0     37     18      0      0     38      0      0
## rs10063667_A      0     18      0      0     18     18     18      0
## rs8093884_A      37      0      0      0      0      0     18      0
## rs10795908_G     18     18     37     18      0      0      0     18
## rs4821456_G      39      0      0      0      0     18      0      0
## rs13058433_G     18      0     18      0      0     18     18     18
## rs7807747_A      18      0     18      0     18      0      0      0
## rs12679812_A      0     18      0     18      0      0     18      0
## rs11981000_G      0      0      0      0      0     18     38      0
## rs10050725_G      0      0      0      0      0     18      0      0
## rs4716858_A      36      0     18     18      0      0      0     18
## rs7840855_G       0     18     18      0      0      0      0     18
## rs818441_A        0      0     18      0      0      0      0     18
## rs4282978_A       0      0     38     37     18      0     37      0
## rs635070_A        0     18     36      0      0      0      0      0
## rs12794303_G      0      0      0      0      0      0      0      0
## rs9291002_A       0     40      0     18      0      0     18     18
## rs4932545_G      18      0     18     18      0     18     18     18
## rs742502_G       18      0      0      0      0     18     18     18
## rs1016090_G       0     18     18     18      0      0      0      0
## rs1732581_A       0      0     18      0      0      0      0      0
## rs750064_G        0      0      0      0     39      0      0      0
## rs4961252_G       0      0      0      0     18      0      0      0
## rs933881_G        0      0      0      0     36      0      0      0
## rs7327037_G       0      0      0      0      0      0      0      0
## rs1882153_A       0      0     18      0      0      0      0      0
## rs2835695_G       0     18      0      0      0      0      0      0
## rs11218557_G      0      0      0      0      0      0     18      0
## rs3095301_G       0      0      0      0     18      0      0      0
## rs3095302_A       0      0      0      0     18      0      0      0
## rs3131003_A       0      0      0      0     18      0      0      0
## rs7725574_C       0      0      0      0      0      0      0      0
## rs7912364_C       0      0      0      0      0      0      0      0
##              iter35 iter36 iter37 iter38 iter39 iter40 iter41 iter42
## rs13279576_A     38    0.0     18     39     18     38     40     18
## rs7931183_A      39   35.0      0     18      0     39     18      0
## rs10837562_G     40    0.0      0     38      0     18     18     40
## rs9296990_A       0   17.5      0     18      0     18     37     39
## rs2331992_G      18   17.5     18      0      0      0     18     38
## rs12476415_G     18    0.0     18     36     40     18     18     18
## rs7430710_A      18   17.5     18     18     18     18     18     18
## rs11257386_A      0   17.5     37      0      0      0      0     18
## rs2149733_A       0   17.5      0     18     18      0     18      0
## rs2168477_A      18   17.5      0     18      0      0     18      0
## rs10412466_G      0    0.0     40      0      0     36      0     18
## rs694625_A        0   17.5     18      0     18     18     18     18
## rs11150589_A      0    0.0     18      0     18     18     18     18
## rs16825228_G      0    0.0      0     18     18     18      0     18
## rs12005670_A      0   17.5      0     18      0      0      0     18
## rs12598978_A      0    0.0     18      0     18     18     18     18
## rs10405944_G     18   17.5     18     18      0      0      0     18
## rs12629469_A      0    0.0      0      0     18      0     38      0
## rs10063667_A      0   17.5     18     18     18     18     18      0
## rs8093884_A       0    0.0      0     37      0     18      0      0
## rs10795908_G      0    0.0     18      0      0      0     39     18
## rs4821456_G      37   17.5      0     18     18      0     18      0
## rs13058433_G      0    0.0     18      0      0      0      0      0
## rs7807747_A      18   17.5      0      0     18      0     18      0
## rs12679812_A     18    0.0     18     18      0      0     18     18
## rs11981000_G      0    0.0      0      0      0      0      0      0
## rs10050725_G     18    0.0      0      0      0     37      0      0
## rs4716858_A      18   39.0      0      0      0      0      0      0
## rs7840855_G       0   17.5      0     18      0      0      0     18
## rs818441_A        0   17.5     18      0      0     18      0     18
## rs4282978_A       0    0.0      0      0      0      0      0     18
## rs635070_A        0   17.5      0      0      0      0     18      0
## rs12794303_G      0    0.0     18     18      0     18     18     18
## rs9291002_A       0    0.0     39      0      0      0     18     18
## rs4932545_G       0    0.0      0      0     18      0     18      0
## rs742502_G        0    0.0     18      0      0      0      0      0
## rs1016090_G      18    0.0      0      0      0     18     18      0
## rs1732581_A       0    0.0      0      0     18      0      0      0
## rs750064_G        0    0.0     38     18      0      0      0      0
## rs4961252_G       0    0.0      0      0      0      0      0      0
## rs933881_G       18    0.0     18      0     18      0      0      0
## rs7327037_G       0   36.0     18      0     18      0      0      0
## rs1882153_A       0    0.0      0      0     18      0      0      0
## rs2835695_G       0    0.0      0      0      0      0      0      0
## rs11218557_G      0    0.0      0      0      0     18      0      0
## rs3095301_G       0    0.0      0      0      0      0      0      0
## rs3095302_A       0    0.0      0      0      0      0      0      0
## rs3131003_A       0    0.0      0      0      0      0      0      0
## rs7725574_C       0   17.5      0      0      0      0     18      0
## rs7912364_C      18    0.0      0     18      0      0     18      0
##              iter43 iter44 iter45 iter46 iter47 iter48 iter49 iter50
## rs13279576_A     18     40   39.0     39     18    0.0     18     38
## rs7931183_A      40      0   38.0     18     39   40.0      0     37
## rs10837562_G     18      0   35.0     40      0    0.0      0      0
## rs9296990_A       0      0    0.0     18     40    0.0     18      0
## rs2331992_G      18      0    0.0     18     18   39.0     40      0
## rs12476415_G      0     18   17.5     36     18    0.0      0      0
## rs7430710_A       0      0   17.5     18     18   17.5     18     18
## rs11257386_A     18      0    0.0      0     18   37.0     18      0
## rs2149733_A      18     18   17.5     18     18   17.5      0     18
## rs2168477_A      18     18    0.0     18     18   17.5     18      0
## rs10412466_G     37      0   17.5      0      0    0.0      0      0
## rs694625_A        0      0   17.5     18     18    0.0      0      0
## rs11150589_A     18      0    0.0      0     18    0.0     18      0
## rs16825228_G      0      0   17.5     18      0    0.0      0      0
## rs12005670_A      0     18    0.0     18      0   17.5     18      0
## rs12598978_A     18      0    0.0      0     18    0.0     18      0
## rs10405944_G     18      0    0.0      0     18   17.5     18      0
## rs12629469_A      0      0    0.0     18      0    0.0     18     18
## rs10063667_A      0      0   17.5      0      0    0.0      0      0
## rs8093884_A       0     39    0.0      0     18   17.5     37      0
## rs10795908_G      0      0    0.0      0     18   17.5      0     18
## rs4821456_G       0      0    0.0     18     37   17.5     18      0
## rs13058433_G     18      0   17.5      0      0   17.5     18     18
## rs7807747_A       0     18   17.5     18      0    0.0      0      0
## rs12679812_A     18      0   17.5      0      0    0.0     18      0
## rs11981000_G      0     18   17.5      0      0    0.0      0      0
## rs10050725_G      0      0   37.0      0      0    0.0      0      0
## rs4716858_A      18      0    0.0      0     36   17.5      0      0
## rs7840855_G       0      0    0.0      0      0   17.5     18      0
## rs818441_A        0      0    0.0      0      0    0.0     18      0
## rs4282978_A      18      0    0.0     18      0    0.0      0      0
## rs635070_A        0      0   17.5      0      0    0.0      0      0
## rs12794303_G      0      0    0.0     18      0   17.5      0      0
## rs9291002_A      18      0    0.0      0      0    0.0      0      0
## rs4932545_G       0      0    0.0     18     18    0.0      0      0
## rs742502_G        0      0    0.0      0      0   17.5     18     18
## rs1016090_G       0      0    0.0     18     18    0.0      0      0
## rs1732581_A       0      0   17.5     18     18   17.5     18      0
## rs750064_G       18      0    0.0      0      0    0.0     36      0
## rs4961252_G       0      0    0.0      0      0    0.0      0     18
## rs933881_G        0      0   36.0      0      0    0.0      0     39
## rs7327037_G      39      0    0.0      0      0    0.0      0     18
## rs1882153_A       0      0   17.5     18     18   17.5     18      0
## rs2835695_G      18      0   17.5      0      0    0.0      0      0
## rs11218557_G      0      0    0.0      0      0   17.5      0      0
## rs3095301_G       0     37   17.5      0      0   17.5      0      0
## rs3095302_A       0     37   17.5      0      0   17.5      0      0
## rs3131003_A       0     37   17.5      0      0   17.5      0      0
## rs7725574_C       0      0    0.0      0     18    0.0      0      0
## rs7912364_C       0      0    0.0     18      0    0.0      0      0
##              iter51 iter52 iter53 iter54 iter55 iter56 iter57 iter58
## rs13279576_A      0      0     38      0   40.0     37      0     38
## rs7931183_A      18      0     18      0   39.0     18     39     18
## rs10837562_G     36      0      0     40   17.5     18     40     40
## rs9296990_A       0      0     18     36    0.0      0     18     18
## rs2331992_G      18      0     37     38    0.0      0      0     37
## rs12476415_G     18     17     18     18   35.5      0      0     18
## rs7430710_A       0      0      0     18   17.5      0      0     18
## rs11257386_A     40      0     18     39   17.5      0      0     18
## rs2149733_A      18      0     18     18   17.5      0     18     18
## rs2168477_A      18     17     18     18   17.5      0      0     18
## rs10412466_G      0     34     18      0    0.0      0     36     18
## rs694625_A        0      0     18      0   17.5     18      0      0
## rs11150589_A     18      0     18      0    0.0     18     18     18
## rs16825228_G      0     17      0     18   35.5      0      0      0
## rs12005670_A      0      0      0      0    0.0      0     18      0
## rs12598978_A     18      0     18      0    0.0     18     18     18
## rs10405944_G     18      0      0      0    0.0      0     18     18
## rs12629469_A     38      0     18     18   17.5     18      0     18
## rs10063667_A     18     17     18     18   17.5      0      0     18
## rs8093884_A       0     37     39      0    0.0     18     18      0
## rs10795908_G     37      0     18     37    0.0      0      0      0
## rs4821456_G      18      0      0     18   17.5      0      0      0
## rs13058433_G      0      0      0      0   17.5     40      0      0
## rs7807747_A      18      0      0     18   17.5      0      0     36
## rs12679812_A     18      0      0      0   17.5     18      0      0
## rs11981000_G     18      0      0      0   17.5      0     18     18
## rs10050725_G      0      0      0     18    0.0      0     37     39
## rs4716858_A      18      0      0      0    0.0     38      0     18
## rs7840855_G      18     17      0     18    0.0      0      0     18
## rs818441_A       18      0      0      0    0.0      0     18      0
## rs4282978_A       0      0      0     18    0.0      0      0      0
## rs635070_A       18      0     18     18   38.0     18      0     18
## rs12794303_G      0      0     18      0    0.0      0      0      0
## rs9291002_A      18      0      0      0    0.0      0      0      0
## rs4932545_G       0     17      0     18    0.0      0      0      0
## rs742502_G        0      0      0      0    0.0     39      0      0
## rs1016090_G      18      0      0     18    0.0     18      0      0
## rs1732581_A       0      0     18      0    0.0      0      0      0
## rs750064_G        0      0     36      0    0.0      0      0      0
## rs4961252_G       0      0     18      0   17.5      0      0      0
## rs933881_G        0     17      0      0    0.0      0      0     18
## rs7327037_G       0     36      0      0    0.0      0      0      0
## rs1882153_A       0      0     18      0    0.0      0      0      0
## rs2835695_G      18      0      0      0    0.0      0      0      0
## rs11218557_G      0     17      0      0    0.0      0     38      0
## rs3095301_G       0      0      0      0    0.0      0      0      0
## rs3095302_A       0      0      0      0    0.0      0      0      0
## rs3131003_A       0      0      0      0    0.0      0      0      0
## rs7725574_C       0      0      0      0   37.0      0      0      0
## rs7912364_C       0      0      0     18    0.0      0      0      0
##              iter59 iter60 iter61 iter62 iter63 iter64 iter65 iter66
## rs13279576_A     18     38     40     37      0      0     40   39.0
## rs7931183_A       0     40     39      0     18     18     38   17.5
## rs10837562_G     38     18      0      0     40      0     37   35.0
## rs9296990_A      18     18     38     18      0      0     18   17.5
## rs2331992_G      18     39      0     18     38      0     39   38.0
## rs12476415_G     18     18     37     18     18      0     18   17.5
## rs7430710_A       0     18      0     18     18     18      0    0.0
## rs11257386_A     36     37     18     18      0     18     36   17.5
## rs2149733_A       0     18     18     18     18     18      0    0.0
## rs2168477_A       0     18     18     18     18     18     18   17.5
## rs10412466_G      0     18     18      0      0      0     18    0.0
## rs694625_A       39      0      0      0      0     18     18    0.0
## rs11150589_A     18      0     18      0     18      0     18   17.5
## rs16825228_G     18     18     18     18      0      0     18    0.0
## rs12005670_A      0      0      0     18     18      0     18   17.5
## rs12598978_A     18      0     18      0     18      0     18   17.5
## rs10405944_G      0     18      0     18     18      0      0   17.5
## rs12629469_A      0     18      0      0      0      0      0   17.5
## rs10063667_A      0     18      0      0      0     18      0    0.0
## rs8093884_A       0     18      0      0     36      0     18    0.0
## rs10795908_G     37     18      0      0      0     18     18    0.0
## rs4821456_G       0      0      0      0     37     18     18   37.0
## rs13058433_G      0      0      0      0      0      0      0    0.0
## rs7807747_A       0      0      0     18      0     18      0    0.0
## rs12679812_A      0      0      0      0     18      0      0    0.0
## rs11981000_G      0      0     18      0      0      0      0    0.0
## rs10050725_G      0      0      0      0     18      0     18   17.5
## rs4716858_A       0      0      0      0      0      0      0    0.0
## rs7840855_G      18      0      0      0      0      0     18    0.0
## rs818441_A        0      0      0     18     18      0     18    0.0
## rs4282978_A      18     18      0      0      0      0      0    0.0
## rs635070_A        0      0      0      0     18     18      0    0.0
## rs12794303_G      0      0      0      0      0      0      0   17.5
## rs9291002_A       0     18      0      0      0      0      0   17.5
## rs4932545_G       0      0      0     18      0      0     18    0.0
## rs742502_G        0      0      0      0      0      0      0    0.0
## rs1016090_G       0      0      0      0      0     18      0    0.0
## rs1732581_A      18      0      0     18      0      0      0    0.0
## rs750064_G       40      0      0      0     18      0      0    0.0
## rs4961252_G      18      0      0     39      0     38      0    0.0
## rs933881_G        0      0      0      0      0      0      0    0.0
## rs7327037_G       0     18      0      0      0     36      0    0.0
## rs1882153_A       0      0      0     18      0      0      0    0.0
## rs2835695_G       0      0     18      0      0      0      0    0.0
## rs11218557_G      0     18     18      0      0      0      0    0.0
## rs3095301_G       0      0     18      0      0      0      0    0.0
## rs3095302_A       0      0     18      0      0      0      0    0.0
## rs3131003_A       0      0     18      0      0      0      0    0.0
## rs7725574_C       0     18      0      0     18      0     18    0.0
## rs7912364_C       0      0      0     18      0      0      0   17.5
##              iter67 iter68 iter69 iter70 iter71 iter72 iter73 iter74
## rs13279576_A     39     18   40.0     36     18     18      0      0
## rs7931183_A      18     18   18.0      0     38      0      0     18
## rs10837562_G     18     18   18.0     40      0     38     40      0
## rs9296990_A      18     37    0.0     18      0     18     18      0
## rs2331992_G       0     40   18.0     18     36     18      0      0
## rs12476415_G      0     18   37.5     18     37      0     18     18
## rs7430710_A      18     18   18.0     18      0     18     18      0
## rs11257386_A      0      0   18.0     18      0     39      0     18
## rs2149733_A       0     18   18.0     18     18     18      0      0
## rs2168477_A       0     18   18.0     18     18     18      0     18
## rs10412466_G     18     18   18.0      0     39      0      0      0
## rs694625_A        0     18   36.0     39     40     37     18      0
## rs11150589_A     18     18    0.0      0      0      0      0      0
## rs16825228_G      0     18   37.5     18     18      0      0      0
## rs12005670_A     18     18   18.0     18      0     18      0      0
## rs12598978_A     18     18    0.0      0      0      0      0      0
## rs10405944_G      0     18    0.0      0     18      0     18      0
## rs12629469_A     18      0   18.0      0      0      0      0     18
## rs10063667_A     18     18   18.0      0     18     18      0      0
## rs8093884_A      18     18    0.0      0     18     18     37      0
## rs10795908_G      0      0   18.0      0      0     36     39      0
## rs4821456_G       0      0    0.0     18     18     18      0     18
## rs13058433_G     18     18    0.0      0      0      0      0      0
## rs7807747_A      18     18    0.0     18     18     18      0     18
## rs12679812_A     18      0    0.0      0     18     18      0      0
## rs11981000_G      0      0   18.0      0      0      0      0      0
## rs10050725_G     38      0    0.0      0      0     18      0     18
## rs4716858_A      18     18    0.0      0      0      0      0      0
## rs7840855_G       0      0   18.0      0      0      0     18      0
## rs818441_A        0     18    0.0     18      0      0      0     18
## rs4282978_A       0      0    0.0      0      0      0     38     18
## rs635070_A        0      0    0.0      0     18      0      0      0
## rs12794303_G     18      0   18.0     18      0     18      0     18
## rs9291002_A      18      0    0.0     18      0      0      0     18
## rs4932545_G       0      0    0.0      0      0      0     18     18
## rs742502_G       18     18    0.0      0      0      0      0      0
## rs1016090_G       0      0    0.0      0      0     18     18      0
## rs1732581_A       0     18   18.0      0     18      0      0      0
## rs750064_G        0     38   39.0      0      0      0      0      0
## rs4961252_G       0      0   18.0      0      0      0      0     18
## rs933881_G       40      0    0.0      0      0      0      0     18
## rs7327037_G      18      0    0.0      0      0      0      0      0
## rs1882153_A       0     18   18.0      0     18      0      0      0
## rs2835695_G       0      0    0.0      0      0     18      0     39
## rs11218557_G      0      0    0.0      0     18      0      0      0
## rs3095301_G       0      0    0.0     18      0     18      0      0
## rs3095302_A       0      0    0.0     18      0     18      0      0
## rs3131003_A       0      0    0.0     18      0     18      0      0
## rs7725574_C       0      0    0.0      0     18      0      0     18
## rs7912364_C      18     18    0.0      0      0      0      0      0
##              iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## rs13279576_A     40     18     18     18     18     18     37      0
## rs7931183_A      37     37     18      0     36     18     40      0
## rs10837562_G     18     40      0      0     40     18      0      0
## rs9296990_A      39      0      0     18     18     18      0      0
## rs2331992_G      18     36     40      0     18      0     18     39
## rs12476415_G     18     18     18     18      0     36     18     18
## rs7430710_A       0      0     18      0     18     18     18     18
## rs11257386_A      0     39     18      0      0      0      0     18
## rs2149733_A       0      0     18     18      0     18     18     18
## rs2168477_A      18      0     18     18      0     18     18     18
## rs10412466_G      0      0     36     39     38     18     18      0
## rs694625_A        0     18      0      0     18     18     18      0
## rs11150589_A      0      0     18     18     18      0      0      0
## rs16825228_G     18     18     18     18      0     18     18     18
## rs12005670_A     18      0     18      0      0      0      0     18
## rs12598978_A      0      0     18     18     18      0      0      0
## rs10405944_G     18      0     18      0     18     18      0     18
## rs12629469_A     38     18      0      0      0      0      0     18
## rs10063667_A      0      0      0      0      0     18     18     18
## rs8093884_A      18      0      0      0      0      0      0      0
## rs10795908_G      0      0      0     18      0      0      0      0
## rs4821456_G       0      0      0     36      0      0      0     18
## rs13058433_G      0      0      0      0     18      0      0      0
## rs7807747_A       0     18      0      0      0      0      0     40
## rs12679812_A      0      0     18      0     18      0      0     18
## rs11981000_G      0      0      0      0      0     40      0      0
## rs10050725_G      0      0      0      0      0     18      0      0
## rs4716858_A       0     18      0      0      0      0      0      0
## rs7840855_G       0      0      0      0      0     18      0      0
## rs818441_A       18      0      0      0      0      0      0      0
## rs4282978_A       0      0     18     40      0      0     36      0
## rs635070_A        0     18      0      0      0      0     38      0
## rs12794303_G      0      0      0      0      0     18     18      0
## rs9291002_A       0      0     38      0     39      0     18      0
## rs4932545_G      18      0      0      0      0      0      0     18
## rs742502_G        0      0      0      0     18      0      0      0
## rs1016090_G       0      0      0     18      0     18      0      0
## rs1732581_A       0     18     18      0     18      0     18      0
## rs750064_G        0      0     18      0      0      0      0      0
## rs4961252_G       0     18      0      0      0      0     39      0
## rs933881_G        0     18      0      0     18      0     18      0
## rs7327037_G       0      0     39      0     18      0     18      0
## rs1882153_A       0     18     18      0     18      0     18      0
## rs2835695_G       0      0      0     18      0     18     18     38
## rs11218557_G     18      0      0      0      0      0      0      0
## rs3095301_G      18      0      0     18      0     38      0     18
## rs3095302_A      18      0      0     18      0     38      0     18
## rs3131003_A      18      0      0     18      0     38      0     18
## rs7725574_C       0     18      0      0      0      0      0      0
## rs7912364_C      18      0      0      0      0      0     18      0
##              iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90
## rs13279576_A     18      0      0     37   17.5     38     18     40
## rs7931183_A      39     38     37     36   37.0     40     40     17
## rs10837562_G     40     18      0     40   38.0     39     37     17
## rs9296990_A      37     36     18     18    0.0     18     18     39
## rs2331992_G      36     40     18     39   17.5     18     38      0
## rs12476415_G      0      0      0     18    0.0     18      0     17
## rs7430710_A      18      0     18     18    0.0     18     18     17
## rs11257386_A      0     18     36      0    0.0     37     36     17
## rs2149733_A       0      0     18     18   17.5      0     18     17
## rs2168477_A      18     18     18      0    0.0     18      0     17
## rs10412466_G     18     39      0     18   39.0      0     18      0
## rs694625_A        0      0      0     18    0.0     18      0      0
## rs11150589_A     18     18      0     18    0.0     18     18     17
## rs16825228_G      0      0      0     18    0.0     18      0      0
## rs12005670_A     18     18     18     18    0.0      0      0      0
## rs12598978_A     18     18      0     18    0.0     18     18     17
## rs10405944_G     18     18      0     18   17.5      0      0      0
## rs12629469_A      0     18     18      0    0.0     18      0     17
## rs10063667_A      0      0      0      0   17.5     18      0     17
## rs8093884_A       0      0      0      0   36.0     18      0      0
## rs10795908_G      0      0     38      0    0.0     18      0      0
## rs4821456_G       0      0     18      0    0.0      0      0      0
## rs13058433_G     18     18     18     18    0.0      0      0      0
## rs7807747_A       0      0      0      0    0.0      0      0      0
## rs12679812_A     18     18      0     18   17.5      0      0      0
## rs11981000_G     18     18      0      0    0.0      0      0      0
## rs10050725_G      0      0      0      0    0.0      0     18     38
## rs4716858_A       0      0     40      0    0.0      0     18      0
## rs7840855_G      18      0     18      0   17.5      0      0     17
## rs818441_A       18     18      0      0   17.5      0      0      0
## rs4282978_A       0      0      0      0    0.0      0      0      0
## rs635070_A       18      0      0      0    0.0     18      0      0
## rs12794303_G     18      0      0      0   17.5      0      0      0
## rs9291002_A       0      0     39     18    0.0      0      0      0
## rs4932545_G       0      0     18      0    0.0      0     18     17
## rs742502_G        0     18     18      0    0.0      0      0      0
## rs1016090_G       0      0     18      0   17.5      0     18     17
## rs1732581_A       0      0     18      0    0.0      0     18     17
## rs750064_G        0      0      0      0   17.5      0      0      0
## rs4961252_G       0      0      0      0    0.0      0      0      0
## rs933881_G        0      0      0      0    0.0     36      0      0
## rs7327037_G       0      0      0      0    0.0      0      0      0
## rs1882153_A       0      0     18      0    0.0      0     18      0
## rs2835695_G       0      0      0      0    0.0      0      0      0
## rs11218557_G      0      0      0      0   17.5      0     39      0
## rs3095301_G       0     18      0      0   17.5      0      0     35
## rs3095302_A       0     18      0      0   17.5      0      0     35
## rs3131003_A       0     18      0      0   17.5      0      0     35
## rs7725574_C      18      0     18     18   17.5      0      0      0
## rs7912364_C       0     18      0     18    0.0      0      0      0
##              iter91 iter92 iter93 iter94 iter95 iter96 iter97 iter98
## rs13279576_A     40   37.0      0     18     37     18     36     40
## rs7931183_A       0   40.0     18      0     38      0     37     18
## rs10837562_G     18    0.0      0     38      0     40     39     18
## rs9296990_A      18   18.0     36     18      0     18     38     18
## rs2331992_G       0   18.0      0      0      0     18      0     18
## rs12476415_G     38   38.5      0     18     18      0     18     18
## rs7430710_A      18   18.0     18      0     18      0     18      0
## rs11257386_A      0    0.0      0      0      0     36      0      0
## rs2149733_A      18   18.0     18     18     18      0      0     18
## rs2168477_A      18   18.0      0     18     18      0      0     18
## rs10412466_G     18    0.0     18     39      0      0      0     39
## rs694625_A       18   18.0     18      0      0      0     40      0
## rs11150589_A     18    0.0     18      0      0      0     18      0
## rs16825228_G     18   38.5      0     18      0      0     18     18
## rs12005670_A     18    0.0      0     18     18     18      0     18
## rs12598978_A     18    0.0     18      0      0      0     18      0
## rs10405944_G      0    0.0      0     18      0     18     18      0
## rs12629469_A     18   18.0      0      0      0     38      0     37
## rs10063667_A      0   18.0      0     18     18      0     18      0
## rs8093884_A       0    0.0      0     18      0      0     18      0
## rs10795908_G      0    0.0      0      0      0     39      0      0
## rs4821456_G       0   18.0      0     18      0     18      0     38
## rs13058433_G     36    0.0     37      0     18     18      0      0
## rs7807747_A       0    0.0     18      0      0      0      0      0
## rs12679812_A      0   18.0      0     18      0      0     18     18
## rs11981000_G     18   18.0      0     18      0     18     18      0
## rs10050725_G      0    0.0      0      0      0      0      0      0
## rs4716858_A       0    0.0      0      0      0     18      0      0
## rs7840855_G       0    0.0      0      0      0     18      0     18
## rs818441_A       18    0.0      0      0     18      0      0     18
## rs4282978_A       0    0.0      0     36     18      0      0      0
## rs635070_A        0    0.0      0     18     39      0      0      0
## rs12794303_G      0    0.0     18      0      0     18     18      0
## rs9291002_A       0   18.0      0      0      0      0      0      0
## rs4932545_G       0    0.0     18      0      0      0      0      0
## rs742502_G       18    0.0     18      0     18     18      0      0
## rs1016090_G      18    0.0      0     18      0      0      0     18
## rs1732581_A       0    0.0      0      0      0      0      0      0
## rs750064_G        0    0.0     18      0      0      0     18      0
## rs4961252_G       0    0.0     18      0     40      0      0      0
## rs933881_G        0    0.0      0      0      0      0      0      0
## rs7327037_G       0    0.0      0      0      0      0      0      0
## rs1882153_A       0    0.0      0      0      0      0      0      0
## rs2835695_G       0    0.0     18     18     18      0      0     18
## rs11218557_G      0    0.0      0      0     18      0     18     36
## rs3095301_G       0    0.0      0     18      0      0      0      0
## rs3095302_A       0    0.0      0     18      0      0      0      0
## rs3131003_A       0    0.0      0     18      0      0      0      0
## rs7725574_C       0   18.0      0      0      0     18      0      0
## rs7912364_C      18   18.0      0      0      0      0      0     18
##              iter99 iter100 total_rank
## rs13279576_A     40       0     2408.0
## rs7931183_A      18      40     2331.5
## rs10837562_G      0       0     2009.5
## rs9296990_A       0       0     1497.5
## rs2331992_G       0       0     1443.0
## rs12476415_G     18       0     1435.0
## rs7430710_A      18       0     1274.0
## rs11257386_A      0       0     1231.5
## rs2149733_A      18      18     1220.0
## rs2168477_A      18      18     1201.0
## rs10412466_G      0       0     1179.5
## rs694625_A        0       0     1050.0
## rs11150589_A      0      18      898.0
## rs16825228_G     18       0      886.5
## rs12005670_A     18      18      880.5
## rs12598978_A      0      18      880.0
## rs10405944_G      0       0      879.0
## rs12629469_A      0       0      862.0
## rs10063667_A      0       0      859.5
## rs8093884_A       0       0      857.5
## rs10795908_G      0       0      810.5
## rs4821456_G       0      18      763.5
## rs13058433_G      0      18      729.0
## rs7807747_A       0       0      650.5
## rs12679812_A      0       0      646.5
## rs11981000_G     18       0      630.0
## rs10050725_G      0       0      625.5
## rs4716858_A       0      18      612.5
## rs7840855_G      18      18      607.5
## rs818441_A        0       0      592.0
## rs4282978_A       0       0      589.5
## rs635070_A        0      18      582.0
## rs12794303_G      0       0      574.0
## rs9291002_A       0      18      572.0
## rs4932545_G       0      18      538.0
## rs742502_G       18      18      528.0
## rs1016090_G       0       0      484.5
## rs1732581_A      18       0      483.5
## rs750064_G        0       0      481.0
## rs4961252_G       0       0      461.0
## rs933881_G        0       0      441.5
## rs7327037_G       0      38      440.0
## rs1882153_A      18       0      430.5
## rs2835695_G       0       0      420.5
## rs11218557_G      0       0      416.5
## rs3095301_G       0       0      414.5
## rs3095302_A       0       0      414.5
## rs3131003_A       0       0      414.5
## rs7725574_C       0      36      414.0
## rs7912364_C       0       0      412.5
write.table(gen_features_comp1_final,file="Comp1_GEN_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")

gen_features_comp2_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),gen_features_comp2)
rownames(gen_features_comp2_final)<-gen_features_comp2_final$GENE
gen_features_comp2_final$GENE<-NULL
gen_features_comp2_final[is.na(gen_features_comp2_final)]<-0
gen_features_comp2_final$total_rank<-rowSums(gen_features_comp2_final)
gen_features_comp2_final<-gen_features_comp2_final[order(-gen_features_comp2_final$total_rank),]
print(head(gen_features_comp2_final,50))
##              iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10
## rs12476415_G    18    18    18     0    38   0.0     0    18     0     18
## rs7931183_A      0    18     0    18    18  17.5    18    18    18      0
## rs9296990_A     18     0     0    37    40  39.0    18     0    39     18
## rs13279576_A    18     0    18    18    18  17.5     0    18    18     18
## rs7430710_A     18     0    18    18     0  17.5    18     0    18     18
## rs2149733_A     18     0     0    18     0  17.5     0    18     0     18
## rs694625_A       0    38    18    38    18   0.0     0     0     0     36
## rs2168477_A     18     0    18    18     0  17.5     0    18     0      0
## rs10837562_G    18    18     0    18    18  17.5    18    18    18     18
## rs7807747_A      0     0     0    39     0   0.0    39    40     0     38
## rs10405944_G     0     0    18     0    18  38.0     0     0    18     18
## rs2331992_G     18     0     0     0     0   0.0    18     0    18     18
## rs8093884_A     39    40     0    18    18   0.0    37     0     0      0
## rs10412466_G    18     0    18     0     0  17.5     0    36     0     18
## rs16825228_G     0     0    18     0    37   0.0     0     0     0     18
## rs10063667_A     0    18     0     0    18  36.0    18    18     0      0
## rs11150589_A    18    18    18    18    18   0.0    18     0     0      0
## rs12005670_A    18    18     0    18     0   0.0     0    18    18      0
## rs11257386_A    18     0     0    18    18   0.0    18     0     0      0
## rs12598978_A    18    18    18    18     0   0.0    18     0     0      0
## rs4821456_G     38     0     0     0     0   0.0     0     0     0     18
## rs635070_A       0     0     0     0     0   0.0    40     0    18      0
## rs12629469_A     0     0    18    18    18   0.0     0    18     0      0
## rs13058433_G     0    36     0     0     0   0.0     0     0    18      0
## rs11981000_G     0     0     0     0    36   0.0    18    18    38      0
## rs10050725_G     0     0     0     0     0  17.5     0    18     0     18
## rs12679812_A     0     0     0     0     0   0.0     0    18    18      0
## rs12794303_G     0     0     0     0     0  17.5     0     0     0     18
## rs7840855_G     18     0    18     0     0  17.5    18     0    18      0
## rs10795908_G     0    18     0     0     0   0.0    18     0     0      0
## rs818441_A      18     0    18    18     0  17.5     0     0    18      0
## rs2835695_G      0     0     0     0     0   0.0    36     0     0      0
## rs4932545_G      0     0     0    18     0   0.0    18    18     0      0
## rs11218557_G    18     0    37    40     0   0.0     0     0     0      0
## rs9291002_A      0     0     0     0     0  17.5    18     0     0      0
## rs742502_G       0    18     0     0     0   0.0     0     0    18      0
## rs1732581_A     18     0    18     0     0   0.0     0     0     0      0
## rs1016090_G      0     0     0     0     0   0.0     0     0     0      0
## rs327826_G      40     0    40    18    18  17.5     0     0     0      0
## rs4716858_A      0    18     0     0     0   0.0     0     0    18     18
## rs4961252_G      0     0     0     0    39  35.0     0     0     0      0
## rs1882153_A     18     0    18     0     0   0.0     0     0     0      0
## rs10217194_A     0    37     0     0     0   0.0     0    39     0      0
## rs4282978_A      0     0    18     0     0   0.0     0    18     0      0
## rs7912364_C      0     0    18    18     0  17.5     0     0     0     18
## rs2212736_G      0     0     0     0     0   0.0    18     0     0      0
## rs1552046_G      0     0     0     0     0   0.0     0    18    18      0
## rs3095301_G      0    18     0     0     0   0.0    18     0     0      0
## rs3095302_A      0    18     0     0     0   0.0    18     0     0      0
## rs3131003_A      0    18     0     0     0   0.0    18     0     0      0
##              iter11 iter12 iter13 iter14 iter15 iter16 iter17 iter18
## rs12476415_G     38     18      0     18      0      0     18      0
## rs7931183_A      18      0     18     18     40     18     18      0
## rs9296990_A       0     18     18     18      0     18     18     37
## rs13279576_A     18     18     18     18     18     18     18     18
## rs7430710_A      18     18     18     18     18     18     18     18
## rs2149733_A      18      0     18      0     18     18     18     18
## rs694625_A        0     37      0      0      0      0     37      0
## rs2168477_A      18     18      0     18     18      0      0      0
## rs10837562_G     18     18     18      0      0     18     18     18
## rs7807747_A       0      0      0      0      0      0     38      0
## rs10405944_G     18      0      0      0      0     18      0     39
## rs2331992_G       0     18      0     18      0      0      0      0
## rs8093884_A       0     40      0     18      0     18      0      0
## rs10412466_G      0      0     18      0      0      0      0     18
## rs16825228_G      0     18      0     18      0      0     18      0
## rs10063667_A     18      0     18      0     18     18     18     18
## rs11150589_A     18      0     18      0      0     18     18      0
## rs12005670_A     18     18      0     18      0     18     18      0
## rs11257386_A      0      0      0      0      0      0     18      0
## rs12598978_A     18      0     18      0      0     18     18      0
## rs4821456_G       0      0      0      0      0      0     18      0
## rs635070_A        0      0      0     39      0      0      0      0
## rs12629469_A     18     18      0      0      0      0      0      0
## rs13058433_G     18      0      0     18      0      0     18     18
## rs11981000_G      0      0      0      0     18     18     18      0
## rs10050725_G     40      0     18      0      0     37     39     38
## rs12679812_A     18      0      0     18      0      0     18      0
## rs12794303_G     18     18      0     18      0      0     18      0
## rs7840855_G       0      0      0      0      0     18      0     18
## rs10795908_G      0      0      0      0      0      0     18      0
## rs818441_A       18      0      0     18      0      0     18      0
## rs2835695_G       0      0     36      0     39     39      0      0
## rs4932545_G       0      0      0      0      0     18     18      0
## rs11218557_G      0      0      0      0     36      0      0      0
## rs9291002_A       0      0      0      0      0      0     18      0
## rs742502_G        0      0      0     18      0      0      0     18
## rs1732581_A       0      0      0      0      0     18      0      0
## rs1016090_G       0      0     18      0     18      0     18     18
## rs327826_G        0     39      0      0      0      0      0      0
## rs4716858_A       0      0      0      0      0      0     18      0
## rs4961252_G       0      0      0     37     18      0      0      0
## rs1882153_A       0      0      0      0      0     18      0      0
## rs10217194_A      0      0      0      0      0      0      0      0
## rs4282978_A       0      0      0      0      0      0     18     18
## rs7912364_C       0      0      0      0      0     18      0     18
## rs2212736_G       0      0     37      0     37     40      0      0
## rs1552046_G      18      0      0     18      0      0     18     36
## rs3095301_G       0     18      0      0      0      0      0      0
## rs3095302_A       0     18      0      0      0      0      0      0
## rs3131003_A       0     18      0      0      0      0      0      0
##              iter19 iter20 iter21 iter22 iter23 iter24 iter25 iter26
## rs12476415_G      0     18     18      0     36     18     37      0
## rs7931183_A      18     18     18     18     18     37     18     18
## rs9296990_A      18      0      0     37      0      0     36      0
## rs13279576_A     18     18     18     18     18     18      0      0
## rs7430710_A      18     18     18      0     18     18     18     18
## rs2149733_A      18     18     18      0      0     18     18      0
## rs694625_A        0     18     39     38     18     18      0      0
## rs2168477_A       0      0     18     18      0     18     18      0
## rs10837562_G     18     18      0      0      0      0     18      0
## rs7807747_A       0     36     18     39      0      0      0     18
## rs10405944_G     18      0      0     18      0     18     38     36
## rs2331992_G      18      0     18     18      0      0      0      0
## rs8093884_A       0      0      0     18     18      0     40     18
## rs10412466_G      0     18      0      0     18      0      0      0
## rs16825228_G      0     18     18      0      0     18     18      0
## rs10063667_A     18     18      0      0      0      0     18      0
## rs11150589_A     18      0     18     18      0      0      0      0
## rs12005670_A      0      0     18     18     18      0     18      0
## rs11257386_A     18     18     18     18     18     18      0      0
## rs12598978_A     18      0     18     18      0      0      0      0
## rs4821456_G       0     18      0      0      0      0      0      0
## rs635070_A        0      0      0     18      0     18     39     18
## rs12629469_A     18      0      0      0      0      0      0      0
## rs13058433_G      0     18      0     18      0      0      0     18
## rs11981000_G      0     37      0      0     18      0      0      0
## rs10050725_G     39      0      0      0     37      0      0      0
## rs12679812_A     18      0      0      0      0      0     18      0
## rs12794303_G     37     18     18      0     18     18      0      0
## rs7840855_G       0      0      0      0      0      0     18     18
## rs10795908_G     18     18     18      0     18     18      0      0
## rs818441_A        0     18      0      0      0     18     18      0
## rs2835695_G       0      0      0     18      0      0      0      0
## rs4932545_G       0      0      0     18      0      0     18      0
## rs11218557_G      0     39     18      0      0      0      0      0
## rs9291002_A       0     38      0     18      0      0      0      0
## rs742502_G        0     18      0     18      0      0      0     18
## rs1732581_A       0     18     18      0      0     18      0      0
## rs1016090_G       0      0      0      0      0      0      0      0
## rs327826_G        0      0      0      0      0      0      0      0
## rs4716858_A      18     18      0      0      0      0      0      0
## rs4961252_G      18      0      0      0      0     18      0     18
## rs1882153_A       0     18     18      0      0      0      0      0
## rs10217194_A      0      0      0     18     18      0      0      0
## rs4282978_A       0     18     18      0      0      0      0      0
## rs7912364_C       0      0      0      0      0     18      0      0
## rs2212736_G       0      0      0      0      0      0      0      0
## rs1552046_G       0      0      0      0      0      0      0      0
## rs3095301_G       0      0      0      0     18      0      0     18
## rs3095302_A       0      0      0      0     18      0      0     18
## rs3131003_A       0      0      0      0     18      0      0     18
##              iter27 iter28 iter29 iter30 iter31 iter32 iter33 iter34
## rs12476415_G      0   17.5      0      0      0     18     18      0
## rs7931183_A      18   17.5      0     18     38      0     18     18
## rs9296990_A       0    0.0     36     18     37     18      0     38
## rs13279576_A      0   17.5     18     18     18     18     18      0
## rs7430710_A       0    0.0     18     18     18     18      0     18
## rs2149733_A       0   17.5     18     18     18     18     18     18
## rs694625_A        0    0.0     18      0      0      0     18     40
## rs2168477_A       0   17.5     18     18     18     18     18     18
## rs10837562_G      0   17.5     18     18     18     18      0     18
## rs7807747_A      18    0.0     37      0     18      0      0      0
## rs10405944_G     18   17.5      0      0      0     18     18     18
## rs2331992_G      18    0.0      0      0      0     40      0     18
## rs8093884_A      18    0.0      0      0      0      0     39      0
## rs10412466_G     36    0.0      0     18     18     38     18     39
## rs16825228_G      0    0.0      0      0      0     18      0      0
## rs10063667_A      0   17.5      0      0     39     18     18      0
## rs11150589_A      0   17.5     18     18      0      0     18      0
## rs12005670_A      0    0.0     18      0      0     18     18     18
## rs11257386_A     18    0.0     18     18      0      0     18     18
## rs12598978_A      0   17.5     18     18      0      0     18      0
## rs4821456_G      18    0.0      0      0      0     39      0      0
## rs635070_A        0   39.0     38      0      0      0      0      0
## rs12629469_A      0   17.5     18      0      0     18      0      0
## rs13058433_G     18    0.0     18      0      0     18     18     18
## rs11981000_G      0    0.0      0      0      0     37     18      0
## rs10050725_G      0    0.0      0      0      0     18      0      0
## rs12679812_A      0   17.5      0     18      0      0     18      0
## rs12794303_G      0    0.0      0      0      0      0      0      0
## rs7840855_G       0   17.5     18      0      0      0      0     18
## rs10795908_G     18   17.5     18     18      0      0      0     18
## rs818441_A        0    0.0     18      0      0      0      0     18
## rs2835695_G       0   38.0      0      0      0      0      0      0
## rs4932545_G      18    0.0     18     18      0     18     18     18
## rs11218557_G      0    0.0      0      0      0      0     38      0
## rs9291002_A       0   17.5      0     18      0      0     37     18
## rs742502_G       18    0.0      0      0      0     18     18     36
## rs1732581_A       0    0.0     18      0      0      0      0      0
## rs1016090_G       0   17.5     18     18      0      0      0      0
## rs327826_G        0    0.0      0     39      0      0      0     18
## rs4716858_A      18    0.0     18     18      0      0      0     18
## rs4961252_G       0    0.0      0      0     18      0      0      0
## rs1882153_A       0    0.0     18      0      0      0      0      0
## rs10217194_A      0    0.0      0      0      0      0      0      0
## rs4282978_A       0    0.0     18     18     18      0     18      0
## rs7912364_C       0    0.0      0      0      0      0      0      0
## rs2212736_G       0    0.0      0      0      0      0      0      0
## rs1552046_G       0    0.0      0     37      0      0     18      0
## rs3095301_G       0    0.0      0      0     18      0      0      0
## rs3095302_A       0    0.0      0      0     18      0      0      0
## rs3131003_A       0    0.0      0      0     18      0      0      0
##              iter35 iter36 iter37 iter38 iter39 iter40 iter41 iter42
## rs12476415_G     18    0.0     36     18     18     36     18     37
## rs7931183_A      18   17.5      0     18      0     18     18      0
## rs9296990_A       0   17.5      0     18      0     40     36     18
## rs13279576_A     18    0.0     18     18     18     18     18     18
## rs7430710_A      18   17.5     18     18     18     18     18     18
## rs2149733_A       0   17.5      0     18     18      0     18      0
## rs694625_A        0   37.0     40      0     39     37     18     40
## rs2168477_A      18   17.5      0     18      0      0     18      0
## rs10837562_G     18    0.0      0     18      0     18     18     18
## rs7807747_A      38   35.0      0      0     40      0     37      0
## rs10405944_G     18   17.5     18     18      0      0      0     18
## rs2331992_G      18   17.5     18      0      0      0     18     18
## rs8093884_A       0    0.0      0     40      0     39      0      0
## rs10412466_G      0    0.0     18      0      0     18      0     18
## rs16825228_G      0    0.0      0     18     18     18      0     18
## rs10063667_A      0   17.5     18     18     18     18     18      0
## rs11150589_A      0    0.0     18      0     18     18     18     18
## rs12005670_A      0   17.5      0     18      0      0      0     18
## rs11257386_A      0   17.5     18      0      0      0      0     18
## rs12598978_A      0    0.0     18      0     18     18     18     18
## rs4821456_G      18   38.0      0     36     36      0     18      0
## rs635070_A        0   17.5      0      0      0      0     38      0
## rs12629469_A      0    0.0      0      0     18      0     18      0
## rs13058433_G      0    0.0     18      0      0      0      0      0
## rs11981000_G      0    0.0      0      0      0      0      0      0
## rs10050725_G     18    0.0      0      0      0     18      0      0
## rs12679812_A     18    0.0     18     18      0      0     18     18
## rs12794303_G      0    0.0     18     18      0     18     18     18
## rs7840855_G       0   17.5      0     18      0      0      0     18
## rs10795908_G      0    0.0     18      0      0      0     18     18
## rs818441_A        0   17.5     18      0      0     18      0     18
## rs2835695_G       0    0.0      0      0      0      0      0      0
## rs4932545_G       0    0.0      0      0     18      0     18      0
## rs11218557_G      0    0.0      0      0      0     18      0      0
## rs9291002_A       0    0.0     18      0      0      0     18     18
## rs742502_G        0    0.0     18      0      0      0      0      0
## rs1732581_A       0    0.0      0      0     18      0      0      0
## rs1016090_G      18    0.0      0      0      0     18     18      0
## rs327826_G        0    0.0      0     39      0     18      0      0
## rs4716858_A      18   17.5      0      0      0      0      0      0
## rs4961252_G       0    0.0      0      0      0      0      0      0
## rs1882153_A       0    0.0      0      0     18      0      0      0
## rs10217194_A      0    0.0      0      0      0      0      0     38
## rs4282978_A       0    0.0      0      0      0      0      0     18
## rs7912364_C      18    0.0      0     18      0      0     18      0
## rs2212736_G       0    0.0      0      0      0      0      0      0
## rs1552046_G       0    0.0      0      0      0      0      0      0
## rs3095301_G       0    0.0      0      0      0      0      0      0
## rs3095302_A       0    0.0      0      0      0      0      0      0
## rs3131003_A       0    0.0      0      0      0      0      0      0
##              iter43 iter44 iter45 iter46 iter47 iter48 iter49 iter50
## rs12476415_G      0     17   17.5     18     18      0      0      0
## rs7931183_A      18      0   17.5     39     18     18      0     18
## rs9296990_A       0      0    0.0     18     18      0     40      0
## rs13279576_A     18     17   17.5     18     18      0     18     18
## rs7430710_A       0      0   17.5     18     18     18     18     18
## rs2149733_A      18     17   17.5     18     18     18      0     18
## rs694625_A        0      0   17.5     18     18      0      0      0
## rs2168477_A      18     17    0.0     18     18     18     18      0
## rs10837562_G     18      0   17.5     18      0      0      0      0
## rs7807747_A       0     17   37.0     38      0      0      0      0
## rs10405944_G     18      0    0.0      0     18     18     18      0
## rs2331992_G      18      0    0.0     18     18     18     18      0
## rs8093884_A       0     17    0.0      0     39     18     18      0
## rs10412466_G     18      0   17.5      0      0      0      0      0
## rs16825228_G      0      0   17.5     18      0      0      0      0
## rs10063667_A      0      0   17.5      0      0      0      0      0
## rs11150589_A     18      0    0.0      0     18      0     18      0
## rs12005670_A      0     17    0.0     18      0     18     18      0
## rs11257386_A     18      0    0.0      0     18     18     18      0
## rs12598978_A     18      0    0.0      0     18      0     18      0
## rs4821456_G       0      0    0.0     18     40     40     18      0
## rs635070_A        0      0   35.0      0      0      0      0      0
## rs12629469_A      0      0    0.0     18      0      0     18     18
## rs13058433_G     18      0   17.5      0      0     36     18     18
## rs11981000_G      0     17   17.5      0      0      0      0      0
## rs10050725_G      0      0   17.5      0      0      0      0      0
## rs12679812_A     18      0   17.5      0      0      0     18      0
## rs12794303_G      0      0    0.0     18      0     18      0      0
## rs7840855_G       0      0    0.0      0      0     18     18      0
## rs10795908_G      0      0    0.0      0     18     18      0     18
## rs818441_A        0      0    0.0      0      0      0     18      0
## rs2835695_G      18      0   39.0      0      0      0      0      0
## rs4932545_G       0      0    0.0     18     18      0      0      0
## rs11218557_G      0      0    0.0      0      0     18      0      0
## rs9291002_A      18      0    0.0      0      0      0      0      0
## rs742502_G        0      0    0.0      0      0     18     18     18
## rs1732581_A       0      0   17.5     18     18     18     18      0
## rs1016090_G       0      0    0.0     18     18      0      0      0
## rs327826_G        0      0    0.0      0      0      0      0      0
## rs4716858_A      18      0    0.0      0     18     18      0      0
## rs4961252_G       0      0    0.0      0      0      0      0     18
## rs1882153_A       0      0   17.5     18     18     18     18      0
## rs10217194_A      0     40    0.0      0     18      0      0      0
## rs4282978_A      18      0    0.0     18      0      0      0      0
## rs7912364_C       0      0    0.0     18      0      0      0      0
## rs2212736_G       0      0   38.0      0      0      0      0      0
## rs1552046_G      37     17    0.0     18      0      0      0      0
## rs3095301_G       0     17   17.5      0      0     18      0      0
## rs3095302_A       0     17   17.5      0      0     18      0      0
## rs3131003_A       0     17   17.5      0      0     18      0      0
##              iter51 iter52 iter53 iter54 iter55 iter56 iter57 iter58
## rs12476415_G     18     34     18     37     18      0      0     38
## rs7931183_A      18      0     18      0     18     18     18     18
## rs9296990_A       0      0     36     18      0      0     18     18
## rs13279576_A      0      0     18      0     18     18      0     18
## rs7430710_A       0      0      0     18     18      0      0     18
## rs2149733_A      18      0     18     18     18      0     18     18
## rs694625_A        0      0     18      0     18     18      0      0
## rs2168477_A      18     17     18     18     18      0      0     18
## rs10837562_G     18      0      0     18     18     18     18     18
## rs7807747_A      38      0      0     18     18      0      0     40
## rs10405944_G     18      0      0      0      0      0     18     18
## rs2331992_G      18      0     18     18      0      0      0     18
## rs8093884_A       0     17     18      0      0     38     40      0
## rs10412466_G      0     17     37      0      0      0     18     18
## rs16825228_G      0     17      0     36     18      0      0      0
## rs10063667_A     18     17     18     18     18      0      0     18
## rs11150589_A     18      0     18      0      0     18     18     18
## rs12005670_A      0      0      0      0      0      0     18      0
## rs11257386_A     18      0     18     18     18      0      0     18
## rs12598978_A     18      0     18      0      0     18     18     18
## rs4821456_G      18      0      0     18     18      0      0      0
## rs635070_A       18      0     18     18     18     40      0     37
## rs12629469_A     18      0     18     18     18     18      0     18
## rs13058433_G      0      0      0      0     18     18      0      0
## rs11981000_G     18      0      0      0     37      0     36     36
## rs10050725_G      0      0      0     40      0      0     18     18
## rs12679812_A     18      0      0      0     18     18      0      0
## rs12794303_G      0      0     18      0      0      0      0      0
## rs7840855_G      18     17      0     18      0      0      0     18
## rs10795908_G     18      0     18     18      0      0      0      0
## rs818441_A       18      0      0      0      0      0     18      0
## rs2835695_G      39      0      0      0      0      0      0      0
## rs4932545_G       0     17      0     18      0      0      0      0
## rs11218557_G      0     17      0      0      0      0     18      0
## rs9291002_A      18      0      0      0      0      0      0      0
## rs742502_G        0      0      0      0      0     18      0      0
## rs1732581_A       0      0     18      0      0      0      0      0
## rs1016090_G      18      0      0     18      0     18      0      0
## rs327826_G       18      0      0      0      0      0      0      0
## rs4716858_A      18      0      0      0      0     18      0     18
## rs4961252_G       0      0     18      0     40      0      0      0
## rs1882153_A       0      0     18      0      0      0      0      0
## rs10217194_A      0     36      0      0      0      0      0      0
## rs4282978_A       0      0      0     18      0      0      0      0
## rs7912364_C       0      0      0     18      0      0      0      0
## rs2212736_G      40      0      0      0      0      0      0      0
## rs1552046_G       0      0      0      0      0      0     18      0
## rs3095301_G       0      0      0      0      0      0      0      0
## rs3095302_A       0      0      0      0      0      0      0      0
## rs3131003_A       0      0      0      0      0      0      0      0
##              iter59 iter60 iter61 iter62 iter63 iter64 iter65 iter66
## rs12476415_G     38     37     18     18     36      0     18   17.5
## rs7931183_A       0     18     18      0     18     18     18   17.5
## rs9296990_A      18     39     18     18      0      0     18   39.0
## rs13279576_A     18     18     18     39      0      0     18   17.5
## rs7430710_A       0     18      0     18     18     18      0    0.0
## rs2149733_A       0     18     18     18     18     18      0    0.0
## rs694625_A       18      0      0      0      0     18     39    0.0
## rs2168477_A       0     18     18     18     18     18     18   17.5
## rs10837562_G     18     18      0      0     18      0     18   17.5
## rs7807747_A       0      0      0     40      0     38      0    0.0
## rs10405944_G      0     18      0     18     38      0      0   38.0
## rs2331992_G      18     18      0     18     18      0     18   17.5
## rs8093884_A       0     18      0      0     18      0     18    0.0
## rs10412466_G      0     18     36      0      0      0     18    0.0
## rs16825228_G     36     36     18     18      0      0     18    0.0
## rs10063667_A      0     18      0      0      0     18      0    0.0
## rs11150589_A     18      0     18      0     18      0     18   17.5
## rs12005670_A      0      0      0     18     18      0     37   17.5
## rs11257386_A     18     18     18     18      0     18     18   17.5
## rs12598978_A     18      0     18      0     18      0     18   17.5
## rs4821456_G       0      0      0      0     18     18     18   17.5
## rs635070_A        0      0      0      0     18     18      0    0.0
## rs12629469_A      0     38      0      0      0      0      0   17.5
## rs13058433_G      0      0      0      0      0      0      0    0.0
## rs11981000_G      0      0     18      0      0      0      0    0.0
## rs10050725_G      0      0      0      0     18      0     18   17.5
## rs12679812_A      0      0      0      0     18      0      0    0.0
## rs12794303_G      0      0      0      0      0      0      0   35.0
## rs7840855_G      18      0      0      0      0      0     18    0.0
## rs10795908_G     18     18      0      0      0     18     18    0.0
## rs818441_A        0      0      0     18     18      0     18    0.0
## rs2835695_G       0      0     39      0      0      0      0    0.0
## rs4932545_G       0      0      0     18      0      0     18    0.0
## rs11218557_G      0     18     37      0      0      0      0    0.0
## rs9291002_A       0     18      0      0      0      0      0   36.0
## rs742502_G        0      0      0      0      0      0      0    0.0
## rs1732581_A      18      0      0     18      0      0      0    0.0
## rs1016090_G       0      0      0      0      0     18      0    0.0
## rs327826_G        0     18      0      0      0      0      0   17.5
## rs4716858_A       0      0      0      0      0      0      0    0.0
## rs4961252_G      18      0      0     18      0     18      0    0.0
## rs1882153_A       0      0      0     18      0      0      0    0.0
## rs10217194_A      0      0      0     37      0      0      0    0.0
## rs4282978_A      18     18      0      0      0      0      0    0.0
## rs7912364_C       0      0      0     18      0      0      0   17.5
## rs2212736_G       0      0      0      0      0      0      0    0.0
## rs1552046_G       0     18      0      0     18      0     18    0.0
## rs3095301_G       0      0     18      0      0      0      0    0.0
## rs3095302_A       0      0     18      0      0      0      0    0.0
## rs3131003_A       0      0     18      0      0      0      0    0.0
##              iter67 iter68 iter69 iter70 iter71 iter72 iter73 iter74
## rs12476415_G      0   37.5     18     18     18      0     18     40
## rs7931183_A      18   18.0     38      0     18      0      0     18
## rs9296990_A      38   18.0      0     18      0     36     18      0
## rs13279576_A     18   18.0     18     18     18     37      0      0
## rs7430710_A      18   18.0     18     18      0     18     18      0
## rs2149733_A       0   18.0     18     18     18     18      0      0
## rs694625_A        0   40.0     18     18     18     18     18      0
## rs2168477_A       0   18.0     18     18     18     18      0     18
## rs10837562_G     18   18.0     18     18      0     18     18      0
## rs7807747_A      37   39.0      0     39     37     40      0     39
## rs10405944_G      0   18.0      0      0     18      0     18      0
## rs2331992_G       0   18.0     18     18     18     18      0      0
## rs8093884_A      40   18.0      0      0     18     39     40      0
## rs10412466_G     18   18.0     18      0     18      0      0      0
## rs16825228_G      0   37.5     18     18     18      0      0      0
## rs10063667_A     18   18.0     18      0     18     18      0      0
## rs11150589_A     18   18.0      0      0      0      0      0      0
## rs12005670_A     18   18.0     18     18      0     18      0      0
## rs11257386_A      0    0.0     18     18      0     18      0     18
## rs12598978_A     18   18.0      0      0      0      0      0      0
## rs4821456_G       0    0.0      0     38     18     18      0     38
## rs635070_A        0    0.0      0      0     38      0      0      0
## rs12629469_A     18    0.0     18      0      0      0      0     37
## rs13058433_G     18   36.0      0      0      0      0      0      0
## rs11981000_G      0    0.0     18      0      0      0      0      0
## rs10050725_G     18    0.0      0      0      0     18      0     36
## rs12679812_A     18    0.0      0      0     18     18      0      0
## rs12794303_G     18    0.0     39     18      0     18      0     18
## rs7840855_G       0    0.0     18      0      0      0     18      0
## rs10795908_G      0    0.0     18      0      0     18     18      0
## rs818441_A        0   18.0      0     18      0      0      0     18
## rs2835695_G       0    0.0      0      0      0     18      0     18
## rs4932545_G       0    0.0      0      0      0      0     18     18
## rs11218557_G      0    0.0      0      0     18      0      0      0
## rs9291002_A      18    0.0      0     18      0      0      0     18
## rs742502_G       18   18.0      0      0      0      0      0      0
## rs1732581_A       0   18.0     18      0     18      0      0      0
## rs1016090_G       0    0.0      0      0      0     18     18      0
## rs327826_G        0    0.0      0      0      0     18      0     18
## rs4716858_A      18   18.0      0      0      0      0      0      0
## rs4961252_G       0    0.0     18      0      0      0      0     18
## rs1882153_A       0   18.0     18      0     18      0      0      0
## rs10217194_A     18    0.0      0      0      0      0      0      0
## rs4282978_A       0    0.0      0      0      0      0     18     18
## rs7912364_C      18   18.0      0      0      0      0      0      0
## rs2212736_G       0    0.0      0      0      0     18      0     18
## rs1552046_G       0    0.0      0      0     40      0      0      0
## rs3095301_G       0    0.0      0     18      0     18      0      0
## rs3095302_A       0    0.0      0     18      0     18      0      0
## rs3131003_A       0    0.0      0     18      0     18      0      0
##              iter75 iter76 iter77 iter78 iter79 iter80 iter81 iter82
## rs12476415_G     18     37     18     38      0     18     36     18
## rs7931183_A      18     18     18      0     18     40     18      0
## rs9296990_A      18      0      0     18     18     18      0      0
## rs13279576_A     18     18     40     18     18     37     18      0
## rs7430710_A       0      0     18      0     18     18     18     18
## rs2149733_A       0      0     18     18      0     18     18     18
## rs694625_A        0     39      0      0     40     18     18      0
## rs2168477_A      18      0     18     18      0     18     18     18
## rs10837562_G     18     18      0      0     18     18      0      0
## rs7807747_A       0     40      0      0      0      0      0     18
## rs10405944_G     18      0     18      0     18     18      0     36
## rs2331992_G      37     18     18      0     18      0     38     18
## rs8093884_A      18      0      0      0      0      0      0      0
## rs10412466_G      0      0     37     18     18     18     18      0
## rs16825228_G     18     36     18     36      0     18     18     18
## rs10063667_A      0      0      0      0      0     18     18     18
## rs11150589_A      0      0     18     18     18      0      0      0
## rs12005670_A     18      0     18      0      0      0      0     18
## rs11257386_A      0     18     18      0      0      0      0     18
## rs12598978_A      0      0     18     18     18      0      0      0
## rs4821456_G       0      0      0     18      0      0      0     18
## rs635070_A        0     18      0      0      0      0     18      0
## rs12629469_A     18     18      0      0      0      0      0     18
## rs13058433_G      0      0      0      0     18      0      0      0
## rs11981000_G      0      0      0      0      0     18      0      0
## rs10050725_G      0      0      0      0      0     36      0      0
## rs12679812_A      0      0     18      0     18      0      0     18
## rs12794303_G      0      0      0      0      0     18     18      0
## rs7840855_G       0      0      0      0      0     18      0      0
## rs10795908_G      0      0      0     18      0      0      0      0
## rs818441_A       18      0      0      0      0      0      0      0
## rs2835695_G       0      0      0     39      0     18     18     18
## rs4932545_G      18      0      0      0      0      0      0     18
## rs11218557_G     36      0      0      0      0      0      0      0
## rs9291002_A       0      0     18      0     18      0     18      0
## rs742502_G        0      0      0      0     18      0      0      0
## rs1732581_A       0     18     18      0     18      0     18      0
## rs1016090_G       0      0      0     18      0     18      0      0
## rs327826_G        0      0      0      0      0      0      0      0
## rs4716858_A       0     18      0      0      0      0      0      0
## rs4961252_G       0     18      0      0      0      0     18      0
## rs1882153_A       0     18     18      0     18      0     18      0
## rs10217194_A     40      0      0      0      0      0      0      0
## rs4282978_A       0      0     18     18      0      0     18      0
## rs7912364_C      18      0      0      0      0      0     18      0
## rs2212736_G       0      0      0     37      0     18     18     18
## rs1552046_G       0      0      0      0      0      0      0      0
## rs3095301_G      18      0      0     18      0     18      0     18
## rs3095302_A      18      0      0     18      0     18      0     18
## rs3131003_A      18      0      0     18      0     18      0     18
##              iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90
## rs12476415_G      0      0      0     36    0.0     18      0     18
## rs7931183_A      18     18     18     37   17.5     18     18     39
## rs9296990_A      18     18     18     18    0.0     36     40     18
## rs13279576_A     18      0      0     18   17.5     18     18     18
## rs7430710_A      18      0     18     18    0.0     18     18     18
## rs2149733_A       0      0     18     18   17.5      0     18     18
## rs694625_A        0      0      0     18    0.0     18      0      0
## rs2168477_A      18     18     18      0    0.0     18      0     18
## rs10837562_G     18     18      0     18   17.5     18     18     18
## rs7807747_A       0      0      0      0    0.0      0      0      0
## rs10405944_G     18     18      0     18   35.0      0      0      0
## rs2331992_G      18     18     18     18   17.5     18     18      0
## rs8093884_A       0      0      0      0   17.5     37      0      0
## rs10412466_G     36     18      0     18   17.5      0     18      0
## rs16825228_G      0      0      0     18    0.0     18      0      0
## rs10063667_A      0      0      0      0   17.5     18      0     18
## rs11150589_A     18     18      0     18    0.0     18     18     18
## rs12005670_A     18     18     18     18    0.0      0      0      0
## rs11257386_A      0     18     18      0    0.0     18     18     36
## rs12598978_A     18     18      0     18    0.0     18     18     18
## rs4821456_G       0      0     18      0    0.0      0      0      0
## rs635070_A       37      0      0      0    0.0     18      0      0
## rs12629469_A      0     18     18      0    0.0     18      0     37
## rs13058433_G     18     18     18     18    0.0      0      0      0
## rs11981000_G     18     39      0      0    0.0      0      0      0
## rs10050725_G      0      0      0      0    0.0      0     36     18
## rs12679812_A     18     18      0     18   17.5      0      0      0
## rs12794303_G     18      0      0      0   17.5      0      0      0
## rs7840855_G      18      0     18      0   17.5      0      0     18
## rs10795908_G      0      0     18      0    0.0     18      0      0
## rs818441_A       18     18      0      0   17.5      0      0      0
## rs2835695_G       0      0      0      0    0.0      0      0      0
## rs4932545_G       0      0     18      0    0.0      0     18     18
## rs11218557_G      0      0      0      0   17.5      0     18      0
## rs9291002_A       0      0     18     18    0.0      0      0      0
## rs742502_G        0     18     18      0    0.0      0      0      0
## rs1732581_A       0      0     18      0    0.0      0     18     18
## rs1016090_G       0      0     18      0   17.5      0     18     18
## rs327826_G        0      0      0      0   39.0     18      0      0
## rs4716858_A       0      0     18      0    0.0      0     18      0
## rs4961252_G       0      0      0      0    0.0      0      0      0
## rs1882153_A       0      0     18      0    0.0      0     18      0
## rs10217194_A      0      0      0      0    0.0      0      0     18
## rs4282978_A       0      0      0      0    0.0      0      0      0
## rs7912364_C       0     18      0     18    0.0      0      0      0
## rs2212736_G       0      0      0      0    0.0      0      0      0
## rs1552046_G       0      0      0      0    0.0      0      0      0
## rs3095301_G       0     37      0      0   17.5      0      0     18
## rs3095302_A       0     37      0      0   17.5      0      0     18
## rs3131003_A       0     37      0      0   17.5      0      0     18
##              iter91 iter92 iter93 iter94 iter95 iter96 iter97 iter98
## rs12476415_G     18     18      0     36     38      0     18     18
## rs7931183_A       0     18     18      0     18      0     18     18
## rs9296990_A      37     36     18     18      0     18     18     18
## rs13279576_A     18     18      0     18     40     18     18     18
## rs7430710_A      18     18     18      0     18      0     18      0
## rs2149733_A      18     18     18     18     18      0      0     18
## rs694625_A       36     18     36      0      0      0     18      0
## rs2168477_A      18     18      0     18     18      0      0     18
## rs10837562_G     18      0      0     18      0     18     18     18
## rs7807747_A       0      0     39      0      0      0      0      0
## rs10405944_G      0      0      0     18      0     18     18      0
## rs2331992_G       0     18      0      0      0     39      0     18
## rs8093884_A       0      0      0     40      0      0     36      0
## rs10412466_G     18      0     18     18      0      0      0     18
## rs16825228_G     18     18      0     18      0      0     18     18
## rs10063667_A      0     18      0     18     18      0     18      0
## rs11150589_A     18      0     18      0      0      0     18      0
## rs12005670_A     18      0      0     18     18     18      0     18
## rs11257386_A      0      0      0      0      0     18      0      0
## rs12598978_A     18      0     18      0      0      0     18      0
## rs4821456_G       0     18      0     38      0     18      0     18
## rs635070_A        0      0      0     37     18      0      0      0
## rs12629469_A     18     18      0      0      0     18      0     18
## rs13058433_G     18      0     18      0     18     36      0      0
## rs11981000_G     18     18      0     18      0     37     18      0
## rs10050725_G      0      0      0      0      0      0      0      0
## rs12679812_A      0     18      0     18      0      0     18     18
## rs12794303_G      0      0     18      0      0     18     18      0
## rs7840855_G       0      0      0      0      0     18      0     18
## rs10795908_G      0      0      0      0      0     18      0      0
## rs818441_A       18      0      0      0     18      0      0     18
## rs2835695_G       0      0     18     39     18      0      0     39
## rs4932545_G       0      0     18      0      0      0      0      0
## rs11218557_G      0      0      0      0     18      0     40     37
## rs9291002_A       0     18      0      0      0      0      0      0
## rs742502_G       18      0     18      0     18     18      0      0
## rs1732581_A       0      0      0      0      0      0      0      0
## rs1016090_G      18      0      0     18      0      0      0     18
## rs327826_G        0     37      0      0      0      0      0      0
## rs4716858_A       0      0      0      0      0     18      0      0
## rs4961252_G       0      0     18      0     18      0      0      0
## rs1882153_A       0      0      0      0      0      0      0      0
## rs10217194_A     40      0      0      0     18      0      0      0
## rs4282978_A       0      0      0     18     18      0      0      0
## rs7912364_C      18     18      0      0      0      0      0     18
## rs2212736_G       0      0     18      0     18      0      0     38
## rs1552046_G       0      0     18      0     18      0      0      0
## rs3095301_G       0      0      0     18      0      0      0      0
## rs3095302_A       0      0      0     18      0      0      0      0
## rs3131003_A       0      0      0     18      0      0      0      0
##              iter99 iter100 total_rank
## rs12476415_G     18       0     1566.0
## rs7931183_A      38      18     1549.0
## rs9296990_A       0       0     1548.5
## rs13279576_A     18       0     1521.5
## rs7430710_A      18       0     1276.5
## rs2149733_A      18      18     1220.5
## rs694625_A        0       0     1211.5
## rs2168477_A      18      18     1202.0
## rs10837562_G      0       0     1167.5
## rs7807747_A       0       0     1140.0
## rs10405944_G      0       0     1035.0
## rs2331992_G       0       0     1034.5
## rs8093884_A       0       0     1020.5
## rs10412466_G      0       0      958.5
## rs16825228_G     18       0      919.0
## rs10063667_A      0       0      900.0
## rs11150589_A      0      18      899.0
## rs12005670_A     18      18      899.0
## rs11257386_A      0       0      881.0
## rs12598978_A      0      18      881.0
## rs4821456_G       0      36      830.5
## rs635070_A        0      18      744.5
## rs12629469_A      0       0      723.0
## rs13058433_G      0      18      683.5
## rs11981000_G     18       0      673.5
## rs10050725_G      0       0      664.5
## rs12679812_A      0       0      646.5
## rs12794303_G      0       0      632.0
## rs7840855_G      18      18      609.0
## rs10795908_G      0       0      593.5
## rs818441_A        0       0      592.5
## rs2835695_G       0       0      584.0
## rs4932545_G       0      18      539.0
## rs11218557_G      0       0      536.5
## rs9291002_A       0      18      524.0
## rs742502_G       18      18      504.0
## rs1732581_A      18       0      485.5
## rs1016090_G       0       0      485.0
## rs327826_G        0       0      470.0
## rs4716858_A       0      18      449.5
## rs4961252_G       0       0      439.0
## rs1882153_A      18       0      431.5
## rs10217194_A      0       0      415.0
## rs4282978_A       0       0      414.0
## rs7912364_C       0       0      413.0
## rs2212736_G       0       0      411.0
## rs1552046_G       0       0      401.0
## rs3095301_G       0       0      377.0
## rs3095302_A       0       0      377.0
## rs3131003_A       0       0      377.0
write.table(gen_features_comp2_final,file="Comp2_GEN_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")


phen_features_comp1_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),phen_features_comp1)
rownames(phen_features_comp1_final)<-phen_features_comp1_final$GENE
phen_features_comp1_final$GENE<-NULL
phen_features_comp1_final[is.na(phen_features_comp1_final)]<-0
phen_features_comp1_final$total_rank<-rowSums(phen_features_comp1_final)
phen_features_comp1_final<-phen_features_comp1_final[order(-phen_features_comp1_final$total_rank),]
print(head(phen_features_comp1_final,50))
##     iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10 iter11
## BMI     4     3     4     4     4     4     4     4     4      4      4
## SI      3     4     2     3     3     3     3     3     2      2      3
## Age     2     2     1     2     2     2     2     2     3      3      2
## Sex     1     1     3     1     1     1     1     1     1      1      1
##     iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19 iter20 iter21
## BMI      4      4      4      4      4      4      3      4      4      4
## SI       2      3      3      3      3      3      4      3      3      3
## Age      3      1      2      2      1      2      1      2      2      1
## Sex      1      2      1      1      2      1      2      1      1      2
##     iter22 iter23 iter24 iter25 iter26 iter27 iter28 iter29 iter30 iter31
## BMI      4      4      4      3      4      4      3      4      4      4
## SI       3      3      3      4      3      3      4      3      3      1
## Age      2      1      1      2      2      2      2      2      1      3
## Sex      1      2      2      1      1      1      1      1      2      2
##     iter32 iter33 iter34 iter35 iter36 iter37 iter38 iter39 iter40 iter41
## BMI      4      4      4      4      4      4      4      4      3      4
## SI       3      3      3      3      3      3      3      3      4      3
## Age      2      1      2      2      2      2      2      2      2      1
## Sex      1      2      1      1      1      1      1      1      1      2
##     iter42 iter43 iter44 iter45 iter46 iter47 iter48 iter49 iter50 iter51
## BMI      4      4      4      4      4      4      4      4      4      4
## SI       3      3      3      3      2      3      2      3      3      3
## Age      2      2      1      2      1      2      3      2      2      2
## Sex      1      1      2      1      3      1      1      1      1      1
##     iter52 iter53 iter54 iter55 iter56 iter57 iter58 iter59 iter60 iter61
## BMI      3      4      4      4      4      4      4      4      4      4
## SI       4      3      2      3      3      3      3      3      3      3
## Age      1      1      1      2      2      2      1      2      2      1
## Sex      2      2      3      1      1      1      2      1      1      2
##     iter62 iter63 iter64 iter65 iter66 iter67 iter68 iter69 iter70 iter71
## BMI      3      4      4      4      3      4      4      4      4      4
## SI       4      3      2      3      4      3      2      3      3      3
## Age      2      2      3      2      2      1      1      2      2      2
## Sex      1      1      1      1      1      2      3      1      1      1
##     iter72 iter73 iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81
## BMI      4      4      4      4      4      3      4      4      4      4
## SI       2      3      2      2      3      4      3      3      3      2
## Age      3      2      3      3      2      1      2      2      2      3
## Sex      1      1      1      1      1      2      1      1      1      1
##     iter82 iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## BMI      4      4      4      3      4      4      4      4      4      4
## SI       3      3      3      4      3      2      3      3      2      3
## Age      2      2      2      2      1      3      2      2      1      1
## Sex      1      1      1      1      2      1      1      1      3      2
##     iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## BMI      4      4      4      3      4      4      4      4       3
## SI       2      2      3      4      2      3      3      3       4
## Age      3      3      1      2      3      2      2      2       2
## Sex      1      1      2      1      1      1      1      1       1
##     total_rank
## BMI        388
## SI         292
## Age        190
## Sex        130
write.table(phen_features_comp1_final,file="Comp1_PHEN_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")

phen_features_comp2_final<-Reduce(function(x,y) merge(x,y,by="GENE",all=TRUE),phen_features_comp2)
rownames(phen_features_comp2_final)<-phen_features_comp2_final$GENE
phen_features_comp2_final$GENE<-NULL
phen_features_comp2_final[is.na(phen_features_comp2_final)]<-0
phen_features_comp2_final$total_rank<-rowSums(phen_features_comp2_final)
phen_features_comp2_final<-phen_features_comp2_final[order(-phen_features_comp2_final$total_rank),]
print(head(phen_features_comp2_final,50))
##     iter1 iter2 iter3 iter4 iter5 iter6 iter7 iter8 iter9 iter10 iter11
## Sex     3     2     2     4     4     4     3     3     4      3      3
## Age     4     1     4     1     1     2     4     4     1      4      2
## SI      1     3     3     3     3     3     2     2     3      1      4
## BMI     2     4     1     2     2     1     1     1     2      2      1
##     iter12 iter13 iter14 iter15 iter16 iter17 iter18 iter19 iter20 iter21
## Sex      1      2      3      1      3      4      4      4      4      4
## Age      4      1      1      4      4      3      3      2      3      1
## SI       3      4      4      3      2      2      1      3      1      2
## BMI      2      3      2      2      1      1      2      1      2      3
##     iter22 iter23 iter24 iter25 iter26 iter27 iter28 iter29 iter30 iter31
## Sex      4      4      4      3      4      1      3      4      1      4
## Age      1      3      3      4      3      2      4      2      4      3
## SI       3      1      1      2      2      4      2      3      3      2
## BMI      2      2      2      1      1      3      1      1      2      1
##     iter32 iter33 iter34 iter35 iter36 iter37 iter38 iter39 iter40 iter41
## Sex      3      3      4      3      4      4      3      2      1      4
## Age      4      4      1      4      3      1      4      3      2      3
## SI       2      2      3      2      2      2      2      4      4      2
## BMI      1      1      2      1      1      3      1      1      3      1
##     iter42 iter43 iter44 iter45 iter46 iter47 iter48 iter49 iter50 iter51
## Sex      3      2      4      2      3      4      1      3      4      3
## Age      4      4      3      3      4      3      4      1      1      4
## SI       1      3      2      4      1      2      2      4      3      2
## BMI      2      1      1      1      2      1      3      2      2      1
##     iter52 iter53 iter54 iter55 iter56 iter57 iter58 iter59 iter60 iter61
## Sex      3      4      2      4      3      3      2      3      2      4
## Age      4      1      4      1      2      4      4      4      4      3
## SI       1      3      3      3      4      1      3      2      3      1
## BMI      2      2      1      2      1      2      1      1      1      2
##     iter62 iter63 iter64 iter65 iter66 iter67 iter68 iter69 iter70 iter71
## Sex      1      4      2      4      4      3      3      4      3      4
## Age      4      3      3      3      3      4      1      3      4      2
## SI       3      2      4      2      1      2      4      2      1      3
## BMI      2      1      1      1      2      1      2      1      2      1
##     iter72 iter73 iter74 iter75 iter76 iter77 iter78 iter79 iter80 iter81
## Sex      3      4      3      4      4      3      2      4      3      4
## Age      2      1      4      3      1      1      4      2      4      1
## SI       4      2      2      2      3      2      3      3      1      3
## BMI      1      3      1      1      2      4      1      1      2      2
##     iter82 iter83 iter84 iter85 iter86 iter87 iter88 iter89 iter90 iter91
## Sex      4      3      3      4      4      2      4      4      3      4
## Age      1      4      4      1      3      4      3      1      4      2
## SI       3      2      2      2      1      1      2      2      1      3
## BMI      2      1      1      3      2      3      1      3      2      1
##     iter92 iter93 iter94 iter95 iter96 iter97 iter98 iter99 iter100
## Sex      3      3      2      2      3      1      3      4       4
## Age      2      2      4      1      4      4      4      3       1
## SI       4      4      3      3      2      3      1      2       2
## BMI      1      1      1      4      1      2      2      1       3
##     total_rank
## Sex        313
## Age        279
## SI         241
## BMI        167
write.table(phen_features_comp2_final,file="Comp2_PHEN_FEATURES.txt",col.names=TRUE,row.names=TRUE,quote=FALSE,sep="\t")

We can perform a final integration on the top ranked features and visualize their connections via e.g. network analysis:

expr_features_comp1_final<-read.delim("Comp1_EXPR_FEATURES.txt",header=TRUE,sep="\t")
expr_features_comp2_final<-read.delim("Comp2_EXPR_FEATURES.txt",header=TRUE,sep="\t")
meth_features_comp1_final<-read.delim("Comp1_METH_FEATURES.txt",header=TRUE,sep="\t")
meth_features_comp2_final<-read.delim("Comp2_METH_FEATURES.txt",header=TRUE,sep="\t")
gen_features_comp1_final<-read.delim("Comp1_GEN_FEATURES.txt",header=TRUE,sep="\t")
gen_features_comp2_final<-read.delim("Comp2_GEN_FEATURES.txt",header=TRUE,sep="\t")
phen_features_comp1_final<-read.delim("Comp1_PHEN_FEATURES.txt",header=TRUE,sep="\t")
phen_features_comp2_final<-read.delim("Comp2_PHEN_FEATURES.txt",header=TRUE,sep="\t")

#inclusion_cutoff<-0.3

#features_expr1<-vector()
#for(i in rownames(expr_features_comp1_final))
#{
#  if(sum(expr_features_comp1_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_expr1<-append(features_expr1,i)
#  }
#}
#features_expr2<-vector()
#for(i in rownames(expr_features_comp2_final))
#{
#  if(sum(expr_features_comp2_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_expr2<-append(features_expr2,i)
#  }
#}
features_expr1<-rownames(expr_features_comp1_final)[1:50]
features_expr2<-rownames(expr_features_comp2_final)[1:50]
print("Gene expression features to keep:")
## [1] "Gene expression features to keep:"
print(unique(c(features_expr1, features_expr2)))
##  [1] "OPRD1"    "SLC2A2"   "CHL1"     "GRAMD2B"  "FOXE1"    "ELFN1"   
##  [7] "GABRA2"   "ARG2"     "TFCP2L1"  "BARX1"    "CLTRN"    "PCOLCE2" 
## [13] "RASGRP1"  "PLA1A"    "COMP"     "MPP1"     "GLRA1"    "GCNT4"   
## [19] "HCN4"     "PRELP"    "RHOT1"    "MRO"      "GAD1"     "NTN1"    
## [25] "DACH2"    "DCX"      "ARL4C"    "TBC1D4"   "CPXM2"    "FFAR4"   
## [31] "SLC24A2"  "NOTUM"    "LRRC2"    "F11"      "CMTR2"    "LSAMP"   
## [37] "CACNG5"   "NIPAL4"   "REEP1"    "TAGLN3"   "SERPINE2" "CLCF1"   
## [43] "C1QTNF1"  "TSKU"     "KCNA1"    "SV2B"     "CA5B"     "FSTL4"   
## [49] "SIX6"     "DKK3"     "CNTN5"    "GNAL"     "NEFL"     "SULF1"   
## [55] "TIAM1"    "NXPH3"    "TSHR"     "SHISAL1"  "PTCHD4"   "SFTPA1"  
## [61] "SYT1"     "KIAA0319" "FSTL5"    "CTSZ"     "LRRTM2"
X.expr_selected_features<-subset(expr,select=unique(c(features_expr1, features_expr2)))

#features_meth1<-vector()
#for(i in rownames(meth_features_comp1_final))
#{
#  if(sum(meth_features_comp1_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_meth1<-append(features_meth1,i)
#  }
#}
#features_meth2<-vector()
#for(i in rownames(meth_features_comp2_final))
#{
#  if(sum(meth_features_comp2_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_meth2<-append(features_meth2,i)
#  }
#}
features_meth1<-rownames(meth_features_comp1_final)[1:50]
features_meth2<-rownames(meth_features_comp2_final)[1:50]
print("Methylation features to keep:")
## [1] "Methylation features to keep:"
print(unique(c(features_meth1, features_meth2)))
##  [1] "cg02988288" "cg02966936" "cg07175985" "cg14527110" "cg12220370"
##  [6] "cg13566279" "cg14490520" "cg03770217" "cg06184251" "cg25934997"
## [11] "cg04577129" "cg25979005" "cg09467248" "cg17826980" "cg21165486"
## [16] "cg02736232" "cg13336515" "cg11515284" "cg26767974" "cg05627498"
## [21] "cg09449232" "cg13176806" "cg14534405" "cg22364465" "cg15275625"
## [26] "cg11743000" "cg04255401" "cg06749277" "cg27044597" "cg24196354"
## [31] "cg03220447" "cg21533994" "cg03726357" "cg08248985" "cg12451325"
## [36] "cg04934500" "cg07270865" "cg15630265" "cg26079959" "cg13970113"
## [41] "cg13090941" "cg26445440" "cg27539060" "cg19484548" "cg24486540"
## [46] "cg09216797" "cg00970981" "cg24794608" "cg12164242" "cg03622758"
## [51] "cg13544025" "cg27179424" "cg22152677" "cg12084792" "cg14228710"
## [56] "cg13559778" "cg07523470" "cg16901379" "cg08571304" "cg04684637"
## [61] "cg12546646" "cg20836795" "cg20189782" "cg27051815" "cg12747056"
## [66] "cg07935632" "cg14679463" "cg17802766"
X.meth_selected_features<-subset(meth,select=unique(c(features_meth1, features_meth2)))

#features_gen1<-vector()
#for(i in rownames(gen_features_comp1_final))
#{
#  if(sum(gen_features_comp1_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_gen1<-append(features_gen1,i)
#  }
#}
#features_gen2<-vector()
#for(i in rownames(gen_features_comp2_final))
#{
#  if(sum(gen_features_comp2_final[i,]==0) < inclusion_cutoff*N_repeat)
#  {
#    features_gen2<-append(features_gen2,i)
#  }
#}
features_gen1<-rownames(gen_features_comp1_final)[1:50]
features_gen2<-rownames(gen_features_comp2_final)[1:50]
print("Genotype features to keep:")
## [1] "Genotype features to keep:"
print(unique(c(features_gen1, features_gen2)))
##  [1] "rs13279576_A" "rs7931183_A"  "rs10837562_G" "rs9296990_A" 
##  [5] "rs2331992_G"  "rs12476415_G" "rs7430710_A"  "rs11257386_A"
##  [9] "rs2149733_A"  "rs2168477_A"  "rs10412466_G" "rs694625_A"  
## [13] "rs11150589_A" "rs16825228_G" "rs12005670_A" "rs12598978_A"
## [17] "rs10405944_G" "rs12629469_A" "rs10063667_A" "rs8093884_A" 
## [21] "rs10795908_G" "rs4821456_G"  "rs13058433_G" "rs7807747_A" 
## [25] "rs12679812_A" "rs11981000_G" "rs10050725_G" "rs4716858_A" 
## [29] "rs7840855_G"  "rs818441_A"   "rs4282978_A"  "rs635070_A"  
## [33] "rs12794303_G" "rs9291002_A"  "rs4932545_G"  "rs742502_G"  
## [37] "rs1016090_G"  "rs1732581_A"  "rs750064_G"   "rs4961252_G" 
## [41] "rs933881_G"   "rs7327037_G"  "rs1882153_A"  "rs2835695_G" 
## [45] "rs11218557_G" "rs3095301_G"  "rs3095302_A"  "rs3131003_A" 
## [49] "rs7725574_C"  "rs7912364_C"  "rs327826_G"   "rs10217194_A"
## [53] "rs2212736_G"  "rs1552046_G"
X.gen_selected_features<-subset(gen,select=unique(c(features_gen1, features_gen2)))

X.phen_selected_features<-phen

  
data<-list(expr=X.expr_selected_features, meth=X.meth_selected_features, 
                 gen=X.gen_selected_features, phen=X.phen_selected_features)
design=matrix(0.1, ncol=length(data), nrow=length(data), dimnames=list(names(data),names(data)))
diag(design)=0
design["expr","meth"]<-0.1
design["meth","expr"]<-0.1
design["meth","phen"]<-0.01
design["phen","meth"]<-0.01
design["expr","gen"]<-0.01
design["gen","expr"]<-0.01
design["meth","gen"]<-0.01
design["gen","meth"]<-0.01
  
ncomp=2
list.keepX = list("expr"=c(30,30), "meth"=c(30,30), "gen"=c(10,10), "phen"=c(4,4))
res = block.splsda(X=data,Y=as.factor(T2D$T2D),ncomp=ncomp,keepX=list.keepX,design=design,
                   scheme="horst",mode="regression",init="svd.single",near.zero.var=TRUE)
## Design matrix has changed to include Y; each block will be
##             linked to Y.
plotIndiv(res,legend=TRUE,title="Human Pancreatic Islets: Individual Omics",ellipse=FALSE,ind.names=TRUE,cex=3)

plotArrow(res,ind.names=TRUE,legend=TRUE,title="Human Pancreatic Islets: Consensus Across Omics")

plotLoadings(res, comp = 1, contrib = 'max', method = 'median')

plotLoadings(res, comp = 2, contrib = 'max', method = 'median')

plotDiablo(res, ncomp = 1)

plotDiablo(res, ncomp = 2)

plotVar(res,var.names=TRUE,style='graphics',legend=TRUE,pch=c(16,17,18,19),cex=c(0.8,0.8,0.8,0.8),col=c('blue','red2',"darkgreen","cyan"))

circosPlot(res,cutoff=0.7,line=FALSE,size.variables=0.5)

cimDiablo(res,margins=c(11,18))

network(res,blocks=c(1,2),cex.node.name=0.6,color.node=c('blue','red2'),breaks=NULL)

network(res,blocks=c(1,3),cex.node.name=0.6,color.node=c('blue','darkgreen'),breaks=NULL)

network(res,blocks=c(1,4),cex.node.name=0.6,color.node=c('blue','cyan'),breaks=NULL)

network(res,blocks=c(2,3),cex.node.name=0.6,color.node=c('red2','darkgreen'),breaks=NULL)

network(res,blocks=c(2,4),cex.node.name=0.6,color.node=c('red2','cyan'),breaks=NULL)

network(res,blocks=c(3,4),cex.node.name=0.6,color.node=c('darkgreen','cyan'),breaks=NULL)

Finally here is the details on the system on which this document was compiled:

sessionInfo()
## R version 3.6.1 (2019-07-05)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.3 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=sv_SE.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=sv_SE.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=sv_SE.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=sv_SE.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] RColorBrewer_1.1-2  ROCit_1.1.1         mixOmics_6.8.5     
##  [4] ggplot2_3.2.1       lattice_0.20-38     MASS_7.3-51.4      
##  [7] VennDiagram_1.6.20  futile.logger_1.4.3 data.table_1.12.2  
## [10] matrixStats_0.54.0 
## 
## loaded via a namespace (and not attached):
##  [1] Rcpp_1.0.2           RSpectra_0.15-0      plyr_1.8.4          
##  [4] compiler_3.6.1       pillar_1.4.2         formatR_1.7         
##  [7] futile.options_1.0.1 tools_3.6.1          digest_0.6.20       
## [10] evaluate_0.14        tibble_2.1.3         gtable_0.3.0        
## [13] pkgconfig_2.0.2      rlang_0.4.0          Matrix_1.2-17       
## [16] igraph_1.2.4.1       parallel_3.6.1       yaml_2.2.0          
## [19] xfun_0.8             gridExtra_2.3        withr_2.1.2         
## [22] stringr_1.4.0        dplyr_0.8.3          knitr_1.24.5        
## [25] tidyselect_0.2.5     ellipse_0.4.1        glue_1.3.1          
## [28] R6_2.4.0             rARPACK_0.11-0       rmarkdown_1.15.1    
## [31] reshape2_1.4.3       tidyr_0.8.3          corpcor_1.6.9       
## [34] purrr_0.3.2          lambda.r_1.2.3       magrittr_1.5        
## [37] scales_1.0.0         htmltools_0.3.6      assertthat_0.2.1    
## [40] colorspace_1.4-1     labeling_0.3         stringi_1.4.3       
## [43] lazyeval_0.2.2       munsell_0.5.0        crayon_1.3.4